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1.

図書
図書
1. Engineering design via surrogate modelling : a practical guide (hbk.)
Alexander I.J. Forrester, András Sóbester, and Andy J. Keane
出版情報: Chichester : John Wiley, c2008  xviii, 210 p., [8] leaves of plates ; 25 cm
シリーズ名: Progress in astronautics and aeronautics ; v. 226
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Preface
About the Authors
Foreword
Prologue
Fundamentals / Part I：
Sampling Plans / 1：
The 'Curse of Dimensionality' and How to Avoid It / 1.1：
Physical versus Computational Experiments / 1.2：
Designing Preliminary Experiments (Screening) / 1.3：
Estimating the Distribution of Elementary Effects / 1.3.1：
Designing a Sampling Plan / 1.4：
Stratification / 1.4.1：
Latin Squares and Random Latin Hypercubes / 1.4.2：
Space-filling Latin Hypercubes / 1.4.3：
Space-filling Subsets / 1.4.4：
A Note on Harmonic Responses / 1.5：
Some Pointers for Further Reading / 1.6：
References
Constructing a Surrogate / 2：
The Modelling Process / 2.1：
Stage One: Preparing the Data and Choosing a Modelling Approach / 2.1.1：
Stage Two: Parameter Estimation and Training / 2.1.2：
Stage Three: Model Testing / 2.1.3：
Polynomial Models / 2.2：
Example One: Aerofoil Drag / 2.2.1：
Example Two: a Multimodal Testcase / 2.2.2：
What About the k-variable Case? / 2.2.3：
Radial Basis Function Models / 2.3：
Fitting Noise-Free Data / 2.3.1：
Radial Basis Function Models of Noisy Data / 2.3.2：
Kriging / 2.4：
Building the Kriging Model / 2.4.1：
Kriging Prediction / 2.4.2：
Support Vector Regression / 2.5：
The Support Vector Predictor / 2.5.1：
The Kernel Trick / 2.5.2：
Finding the Support Vectors / 2.5.3：
Finding [mu] / 2.5.4：
Choosing C and [epsilon] / 2.5.5：
Computing [epsilon]: v-SVR / 2.5.6：
The Big(ger) Picture / 2.6：
Exploring and Exploiting a Surrogate / 3：
Searching the Surrogate / 3.1：
Infill Criteria / 3.2：
Prediction Based Exploitation / 3.2.1：
Error Based Exploration / 3.2.2：
Balanced Exploitation and Exploration / 3.2.3：
Conditional Likelihood Approaches / 3.2.4：
Other Methods / 3.2.5：
Managing a Surrogate Based Optimization Process / 3.3：
Which Surrogate for What Use? / 3.3.1：
How Many Sample Plan and Infill Points? / 3.3.2：
Convergence Criteria / 3.3.3：
Search of the Vibration Isolator Geometry Feasibility Using Kriging Goal Seeking / 3.4：
Advanced Concepts / Part II：
Visualization / 4：
Matrices of Contour Plots / 4.1：
Nested Dimensions / 4.2：
Reference
Constraints / 5：
Satisfaction of Constraints by Construction / 5.1：
Penalty Functions / 5.2：
Example Constrained Problem / 5.3：
Using a Kriging Model of the Constraint Function / 5.3.1：
Using a Kriging Model of the Objective Function / 5.3.2：
Expected Improvement Based Approaches / 5.4：
Expected Improvement With Simple Penalty Function / 5.4.1：
Constrained Expected Improvement / 5.4.2：
Missing Data / 5.5：
Imputing Data for Infeasible Designs / 5.5.1：
Design of a Helical Compression Spring Using Constrained Expected Improvement / 5.6：
Summary / 5.7：
Infill Criteria with Noisy Data / 6：
Regressing Kriging / 6.1：
Searching the Regression Model / 6.2：
Re-Interpolation / 6.2.1：
Re-Interpolation With Conditional Likelihood Approaches / 6.2.2：
A Note on Matrix Ill-Conditioning / 6.3：
Exploiting Gradient Information / 6.4：
Obtaining Gradients / 7.1：
Finite Differencing / 7.1.1：
Complex Step Approximation / 7.1.2：
Adjoint Methods and Algorithmic Differentiation / 7.1.3：
Gradient-enhanced Modelling / 7.2：
Hessian-enhanced Modelling / 7.3：
Multi-fidelity Analysis / 7.4：
Co-Kriging / 8.1：
One-variable Demonstration / 8.2：
Choosing X[subscript c] and X[subscript e] / 8.3：
Multiple Design Objectives / 8.4：
Pareto Optimization / 9.1：
Multi-objective Expected Improvement / 9.2：
Design of the Nowacki Cantilever Beam Using Multi-objective, Constrained Expected Improvement / 9.3：
Design of a Helical Compression Spring Using Multi-objective, Constrained Expected Improvement / 9.4：
Example Problems / 9.5：
One-Variable Test Function / A.1：
Branin Test Function / A.2：
Aerofoil Design / A.3：
The Nowacki Beam / A.4：
Multi-objective, Constrained Optimal Design of a Helical Compression Spring / A.5：
Novel Passive Vibration Isolator Feasibility / A.6：
Index
Preface
About the Authors
Foreword
2.

図書
図書
2. Nonlinear fiber optics
Govind P. Agrawal
出版情報: Boston ; Tokyo : Academic Press, c1989  xii, 342 p. ; 24 cm
シリーズ名: Quantum electronics : principles and applications
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Preface
Introduction / 1：
Historical Perspective / 1.1：
Fiber Characteristics / 1.2：
Material and Fabrication / 1.2.1：
Fiber Losses / 1.2.2：
Chromatic Dispersion / 1.2.3：
Polarization-Mode Dispersion / 1.2.4：
Fiber Nonlinearities / 1.3：
Nonlinear Refraction / 1.3.1：
Stimulated Inelastic Scattering / 1.3.2：
Importance of Nonlinear Effects / 1.3.3：
Overview / 1.4：
Problems
References
Pulse Propagation in Fibers / 2：
Maxwell's Equations / 2.1：
Fiber Modes / 2.2：
Eigenvalue Equation / 2.2.1：
Single-Mode Condition / 2.2.2：
Characteristics of the Fundamental Mode / 2.2.3：
Pulse-Propagation Equation / 2.3：
Nonlinear Pulse Propagation / 2.3.1：
Higher-Order Nonlinear Effects / 2.3.2：
Numerical Methods / 2.4：
Split-Step Fourier Method / 2.4.1：
Finite-Difference Methods / 2.4.2：
Group-Velocity Dispersion / 3：
Different Propagation Regimes / 3.1：
Dispersion-Induced Pulse Broadening / 3.2：
Gaussian Pulses / 3.2.1：
Chirped Gaussian Pulses / 3.2.2：
Hyperbolic-Secant Pulses / 3.2.3：
Super-Gaussian Pulses / 3.2.4：
Experimental Results / 3.2.5：
Third-Order Dispersion / 3.3：
Changes in Pulse Shape / 3.3.1：
Broadening Factor / 3.3.2：
Arbitrary-Shape Pulses / 3.3.3：
Ultrashort-Pulse Measurements / 3.3.4：
Dispersion Management / 3.4：
GVD-Induced Limitations / 3.4.1：
Dispersion Compensation / 3.4.2：
Compensation of Third-Order Dispersion / 3.4.3：
Self-Phase Modulation / 4：
SPM-Induced Spectral Broadening / 4.1：
Nonlinear Phase Shift / 4.1.1：
Changes in Pulse Spectra / 4.1.2：
Effect of Pulse Shape and Initial Chirp / 4.1.3：
Effect of Partial Coherence / 4.1.4：
Effect of Group-Velocity Dispersion / 4.2：
Pulse Evolution / 4.2.1：
Optical Wave Breaking / 4.2.2：
Effect of Third-Order Dispersion / 4.2.4：
Self-Steepening / 4.3：
Effect of GVD on Optical Shocks / 4.3.2：
Intrapulse Raman Scattering / 4.3.3：
Optical Solitons / 5：
Modulation Instability / 5.1：
Linear Stability Analysis / 5.1.1：
Gain Spectrum / 5.1.2：
Experimental Observation / 5.1.3：
Ultrashort Pulse Generation / 5.1.4：
Impact on Lightwave Systems / 5.1.5：
Fiber Solitons / 5.2：
Inverse Scattering Method / 5.2.1：
Fundamental Soliton / 5.2.2：
Higher-Order Solitons / 5.2.3：
Experimental Confirmation / 5.2.4：
Soliton Stability / 5.2.5：
Other Types of Solitons / 5.3：
Dark Solitons / 5.3.1：
Dispersion-Managed Solitons / 5.3.2：
Bistable Solitons / 5.3.3：
Perturbation of Solitons / 5.4：
Perturbation Methods / 5.4.1：
Soliton Amplification / 5.4.2：
Soliton Interaction / 5.4.4：
Higher-Order Effects / 5.5：
Propagation of Femtosecond Pulses / 5.5.1：
Polarization Effects / 6：
Nonlinear Birefringence / 6.1：
Origin of Nonlinear Birefringence / 6.1.1：
Coupled-Mode Equations / 6.1.2：
Elliptically Birefringent Fibers / 6.1.3：
Nondispersive XPM / 6.2：
Optical Kerr Effect / 6.2.2：
Pulse Shaping / 6.2.3：
Evolution of Polarization State / 6.3：
Analytic Solution / 6.3.1：
Poincare-Sphere Representation / 6.3.2：
Polarization Instability / 6.3.3：
Polarization Chaos / 6.3.4：
Vector Modulation Instability / 6.4：
Low-Birefringence Fibers / 6.4.1：
High-Birefringence Fibers / 6.4.2：
Isotropic Fibers / 6.4.3：
Birefringence and Solitons / 6.4.4：
Soliton-Dragging Logic Gates / 6.5.1：
Vector Solitons / 6.5.4：
Random Birefringence / 6.6：
Polarization State of Solitons / 6.6.1：
Cross-Phase Modulation / 7：
XPM-Induced Nonlinear Coupling / 7.1：
Nonlinear Refractive Index / 7.1.1：
Coupled NLS Equations / 7.1.2：
Propagation in Birefringent Fibers / 7.1.3：
XPM-Induced Modulation Instability / 7.2：
XPM-Paired Solitons / 7.2.1：
Bright-Dark Soliton Pair / 7.3.1：
Bright-Gray Soliton Pair / 7.3.2：
Other Soliton Pairs / 7.3.3：
Spectral and Temporal Effects / 7.4：
Asymmetric Spectral Broadening / 7.4.1：
Asymmetric Temporal Changes / 7.4.2：
Applications of XPM / 7.4.3：
XPM-Induced Pulse Compression / 7.5.1：
XPM-Induced Optical Switching / 7.5.2：
XPM-Induced Nonreciprocity / 7.5.3：
Stimulated Raman Scattering / 8：
Basic Concepts / 8.1：
Raman-Gain Spectrum / 8.1.1：
Raman Threshold / 8.1.2：
Coupled Amplitude Equations / 8.1.3：
Quasi-Continuous SRS / 8.2：
Single-Pass Raman Generation / 8.2.1：
Raman Fiber Lasers / 8.2.2：
Raman Fiber Amplifiers / 8.2.3：
Raman-Induced Crosstalk / 8.2.4：
SRS with Short Pump Pulses / 8.3：
Pulse-Propagation Equations / 8.3.1：
Nondispersive Case / 8.3.2：
Effects of GVD / 8.3.3：
Synchronously Pumped Raman Lasers / 8.3.4：
Soliton Effects / 8.4：
Raman Solitons / 8.4.1：
Raman Soliton Lasers / 8.4.2：
Soliton-Effect Pulse Compression / 8.4.3：
Effect of Four-Wave Mixing / 8.5：
Stimulated Brillouin Scattering / 9：
Physical Process / 9.1：
Brillouin-Gain Spectrum / 9.1.2：
Quasi-CW SBS / 9.2：
Coupled Intensity Equations / 9.2.1：
Brillouin Threshold / 9.2.2：
Gain Saturation / 9.2.3：
Dynamic Aspects / 9.2.4：
Relaxation Oscillations / 9.3.1：
Modulation Instability and Chaos / 9.3.3：
Transient Regime / 9.3.4：
Brillouin Fiber Lasers / 9.4：
CW Operation / 9.4.1：
Pulsed Operation / 9.4.2：
SBS Applications / 9.5：
Brillouin Fiber Amplifiers / 9.5.1：
Fiber Sensors / 9.5.2：
Parametric Processes / 10：
Origin of Four-Wave Mixing / 10.1：
Theory of Four-Wave Mixing / 10.2：
Approximate Solution / 10.2.1：
Effect of Phase Matching / 10.2.3：
Ultrafast FWM / 10.2.4：
Phase-Matching Techniques / 10.3：
Physical Mechanisms / 10.3.1：
Phase Matching in Multimode Fibers / 10.3.2：
Phase Matching in Single-Mode Fibers / 10.3.3：
Phase Matching in Birefringent Fibers / 10.3.4：
Parametric Amplification / 10.4：
Gain and Bandwidth / 10.4.1：
Pump Depletion / 10.4.2：
Parametric Amplifiers / 10.4.3：
Parametric Oscillators / 10.4.4：
FWM Applications / 10.5：
Wavelength Conversion / 10.5.1：
Phase Conjugation / 10.5.2：
Squeezing / 10.5.3：
Supercontinuum Generation / 10.5.4：
Second-Harmonic Generation / 10.6：
Physical Mechanism / 10.6.1：
Simple Theory / 10.6.3：
Quasi-Phase-Matching Technique / 10.6.4：
Decibel Units / Appendix A：
Acronyms / Appendix B：
Index
Preface
Introduction / 1：
Historical Perspective / 1.1：
3.

図書
図書
3. Knowledge-based neurocomputing : a fuzzy logic approach (hbk)
Eyal Kolman and Michael Margaliot
出版情報: Berlin : Springer, c2009  xv, 100 p. ; 24 cm
シリーズ名: Studies in fuzziness and soft computing ; 234
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Preface
List of Abbreviations
List of Symbols
Introduction / 1：
Artificial Neural Networks (ANNs) / 1.1：
Fuzzy Rule-Bases (FRBs) / 1.2：
The ANN-FRB Synergy / 1.3：
Knowledge-Based Neurocomputing / 1.4：
Knowledge Extraction from ANNs / 1.4.1：
Knowledge-Based Design of ANNs / 1.4.2：
The FARB: A Neuro-fuzzy Equivalence / 1.5：
The FARB / 2：
Definition / 2.1：
Input-Output Mapping / 2.2：
The FARB-ANN Equivalence / 3：
The FARB and Feedforward ANNs / 3.1：
Example 1: Knowledge Extraction from a Feedforward ANN / 3.1.1：
Example 2: Knowledge-Based Design of a Feedforward ANN / 3.1.2：
The FARB and First-Order RNNs / 3.2：
First Approach / 3.2.1：
Example 3: Knowledge Extraction from a Simple RNN / 3.2.2：
Second Approach / 3.2.3：
Third Approach / 3.2.4：
Example 4: Knowledge Extraction from an RNN / 3.2.5：
Example 5: Knowledge-Based Design of an RNN / 3.2.6：
The FARB and Second-Order RNNs / 3.3：
Summary / 3.4：
Rule Simplification / 4：
Sensitivity Analysis / 4.1：
A Procedure for Simplifying a FARB / 4.2：
Knowledge Extraction Using the FARB / 5：
The Iris Classification Problem / 5.1：
The LED Display Recognition Problem / 5.2：
FARB Simplification / 5.2.1：
Analysis of the FRB / 5.2.3：
Formal Languages / 5.3：
Formal Languages and RNNs / 5.3.2：
The Trained RNN / 5.3.3：
The Direct Approach / 5.3.4：
The Modular Approach / 6.1.1：
The Counter Module / 6.2.1：
The Sequence-Counter Module / 6.2.2：
The String-Comparator Module / 6.2.3：
The String-to-Num Converter Module / 6.2.4：
The Num-to-String Converter Module / 6.2.5：
The Soft Threshold Module / 6.2.6：
KBD of an RNN for Recognizing the AB Language / 6.2.7：
KBD of an RNN for Recognizing the Balanced Parentheses Language / 6.2.9：
Conclusions and Future Research / 6.2.10：
Future Research / 7.1：
Regularization of Network Training / 7.1.1：
Extracting Knowledge during the Learning Process / 7.1.2：
Knowledge Extraction from Support Vector Machines / 7.1.3：
Knowledge Extraction from Trained Networks / 7.1.4：
Proofs / A：
Details of the LED Recognition Network / B：
References
Index
Preface
List of Abbreviations
List of Symbols
4.

図書
図書
4. Fuzzy logic-based algorithms for video de-interlacing
Piedad Brox, Iluminada Baturone, and Santiago Sánchez-Solano
出版情報: Berlin : Springer Verlag, c2010  x, 174 p. ; 24 cm
シリーズ名: Studies in fuzziness and soft computing ; 246
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Basic Concepts / 1：
Television Transmission Systems / 1.1：
Monochrome Television Systems / 1.1.1：
Analog Color Television Broadcast Systems / 1.1.2：
Digital Color Television Broadcast Systems / 1.1.3：
Equipment for Broadcasting Television Images / 1.2：
Video Cameras / 1.2.1：
Movie Cameras / 1.2.2：
Film-to-Video Transference: Pull-Down Process / 1.2.3：
The Need of De-Interlacing / 1.3：
Review of De-Interlacing Algorithms / 1.3.1：
Implementation of Video De-Interlacing / 1.4：
Consumer Video Processing Chips / 1.4.1：
De-Interlacing Implementations Based on DSPs, FPGAs and IP Cores / 1.4.2：
The Role of Fuzzy Logic in Video Processing / 1.5：
Basic Concepts of Fuzzy Logic Theory / 1.5.1：
CAD Tools for Designing Fuzzy Systems / 1.5.2：
Conclusions / 1.6：
References
Fuzzy Motion-Adaptive Algorithm for Video De-Interlacing / 2：
Motion-Adaptive De-Interlacing / 2.1：
Van de Ville et al. Proposal / 2.2：
A New Fuzzy Motion-Adaptive De-Interlacing Algorithm / 2.3：
Simulation Results / 2.4：
Results on Benchmark Video Sequences / 2.4.1：
Detailed Results on Three Sequences / 2.4.2：
Design Options of the Fuzzy Motion-Adaptive Algorithm / 2.5：
Convolution Mask Options / 3.1：
Rule Base Options / 3.1.1：
Tuning of Membership Function and Consequent Parameters / 3.2.1：
Reference / 3.2.2：
Fuzzy Motion-Adaptive De-Interlacing with Edge-Adaptive Spatial Interpolation / 4：
Basic Fuzzy-ELA Algorithm / 4.1：
Determination of the Membership Function Parameters / 4.1.1：
Performance of the Basic Fuzzy-ELA Algorithm / 4.1.2：
Modifications of the Basic Fuzzy-ELA Algorithm / 4.2：
Recursive Fuzzy-ELA Algorithm / 4.2.1：
ELA 5+5 and Fuzzy-ELA 5+5 Algorithm / 4.2.2：
Improved Fuzzy-ELA 5+5 Algorithm / 4.2.3：
Comparison of the Fuzzy-ELA Algorithms / 4.2.4：
Robustness of Fuzzy Proposals against Noise / 4.2.5：
Fuzzy Motion Adaptive Algorithm with the 'Improved Fuzzy-ELA 5+5' as Spatial Interpolator / 4.2.6：
Fuzzy Motion-Adaptive De-Interlacing with Smart Temporal Interpolation / 4.3：
A Smart Temporal Interpolator / 5.1：
Morphological Operations / 5.1.1：
Performance of the Proposed Algorithm / 5.2：
Evolution of the Fuzzy De-Interlacing Proposals / 5.3：
Comparison with MC De-Interlacing Methods / 5.4：
Glossary / 5.5：
Index
Basic Concepts / 1：
Television Transmission Systems / 1.1：
Monochrome Television Systems / 1.1.1：
5.

図書
図書
5. Analytical methods in fuzzy modeling and control
Jacek Kluska
出版情報: Berlin : Springer, c2009  xxvi, 251 p. ; 24 cm
シリーズ名: Studies in fuzziness and soft computing ; v.241
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Introduction / 1：
MISO Takagi-Sugeno Fuzzy System with Linear Membership Functions / 2：
Perfect Approximation of Nonlinear Functions Using the Simplest Takagi-Sugeno Model / 2.1：
Assumptions and Linguistic Interpretation of Linear Membership Functions / 2.2：
Compact Description of the MISO TS System / 2.3：
Crisp Output of the Zero-Order MISO P1-TS System / 2.4：
Completeness and Noncontradiction in Rule-Based Systems Defined by Metarules / 2.5：
Matrix Description of the MIMO Fuzzy Rule-Based System / 2.6：
Equivalence Problem in the Rule-Based Systems / 2.7：
Summary / 2.8：
Recursion in TS Systems with Two Fuzzy Sets for Every Input / 3：
Some Features of the Fundamental Matrix and Its Inverse / 3.1：
Theorem on Recursion for P1-TS Systems / 3.2：
Rule-Base Decomposition / 3.2.1：
Crisp Output Calculation for P1-TS System Using Recursion / 3.2.2：
Recursion in More General TS Systems with Two Fuzzy Sets for Every Input / 3.3：
MIMO TS Systems with Inference Concerning the Structure Parameters / 3.4：
Boundedness of P1-TS Systems / 3.5：
Fuzzy Rule-Based Systems with Polynomial Membership Functions / 3.6：
TS Systems with Two Polynomial Membership Functions for Every Input / 4.1：
The Normalized Membership Functions for P2-TS Systems / 4.2：
SISO P2-TS System / 4.3：
P2-TS System with Two and More Inputs / 4.4：
Rule-Base Structure for Two-Inputs-One-Output P2-TS System / 4.4.1：
Rule-Base Structure for Three-Inputs-One-Output P2-TS System / 4.4.2：
The Fundamental Matrix for MISO P2-TS System / 4.5：
Recursion in MISO P2-TS Systems / 4.6：
Crisp Output Calculation for P2-TS System Using Recursion / 4.6.1：
Recursion in More General TS Systems with Three Fuzzy Sets for Every Input / 4.7：
Comprehensive Study and Applications of P1-TS Systems / 4.8：
P1-TS Systems with Two Inputs / 5.1：
General Case / 5.1.1：
A Simple Controller Design for a Milk of Lime Blending Tank / 5.1.2：
P1-TS Systems with Inputs and Outputs from the Unity Interval / 5.1.3：
P1-TS Fuzzy Systems with Three Inputs / 5.2：
Examples of Highly Interpretable P1-TS Systems with Three Inputs / 5.2.1：
Examples of P1-TS Systems with Four and More Inputs / 5.3：
Low Order Atmospheric Circulation Model / 5.3.1：
Induction Motor Model / 5.3.2：
Acclimatization Chamber Model / 5.3.3：
Optimal Fuzzy Control System Design for Second Order Plant / 5.4：
Highly Interpretable Fuzzy Rules for PID Controller / 5.4.1：
Optimal PID Fuzzy Controller for Linear Second Order Plant / 5.4.2：
PD-Like Optimal Controller for Nonlinear Second Order Plant / 5.4.3：
P1-TS System as Controller with Variable Gains / 5.5：
Exact Modeling of Single-Input Dynamical Systems / 5.6：
Exact Modeling of MIMO Linear Dynamical Systems / 5.7：
Strong Triangular Fuzzy Partition / 5.8：
Linearity Condition for P1-TS Systems / 5.9：
The First-Order P1-TS Systems / 5.10：
Zero-Order TS System with Contradictory Rule-Base / 5.11：
Modeling of Multilinear Dynamical Systems from Experimental Data / 5.12：
Problem Statement / 6.1：
Problem Solution / 6.2：
Analytical Solution for Dynamical Systems with Two Variables / 6.3：
Estimation of P1-TS Model by Recursive Least Squares / 6.4：
Binary Classification Using P1-TS Rule Scheme / 6.5：
Problem Description / 7.1：
The Fuzzy Rules with Proximity Degrees / 7.2：
Binary Classifier Equation / 7.3：
P1-TS System with Similarity Degrees as Optimal Binary Classifier / 7.4：
The Regularization Algorithm and Support Vector Machines / 7.5：
Kronecker Product of Matrices / 7.6：
Generators and Fundamental Matrices for P1-TS Systems / B：
Formulas for n = 1 / B.1：
Vertices of the Interval D1 = [-?1, ?1] / B.1.1：
Generator / B.1.2：
Fundamental Matrix and Its Inverse / B.1.3：
Formulas for n = 2 / B.2：
Vertices of the Rectangle D2 = [-?1, ?1] × [-?2, ?2] / B.2.1：
Formulas for n = 3 / B.2.2：
Vertices of the Cuboid D3 = [-?1, ?1] × [-?2, ?2] × [-?3, ?3] / B.3.1：
Formulas for n = 4 / B.3.2：
Vertices of the Hypercuboid D4 = [-?1, ?1] × ... × [-?4, ?4] / B.4.1：
Proofs of Theorems, Remarks and Algorithms / B.4.2：
Proof of Remark 3.2 / C.1：
Proof of Remark 3.3 / C.2：
Proof of Corollary 5.27 / C.3：
Proof of RLS Algorithm from Section 6.4 / C.4：
References
Index
Introduction / 1：
MISO Takagi-Sugeno Fuzzy System with Linear Membership Functions / 2：
Perfect Approximation of Nonlinear Functions Using the Simplest Takagi-Sugeno Model / 2.1：
6.

図書
図書
6. Mathematics of fuzziness : basic issues
Xuzhu Wang, Da Ruan and Etienne E Kerre
出版情報: Berlin : Springer, c2009  xi, 219 p. ; 25 cm
シリーズ名: Studies in fuzziness and soft computing ; 245
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Preliminaries / 1：
Sets / 1.1：
Relations / 1.2：
Mappings and Algebraic Systems / 1.3：
Lattices / 1.4：
Special Lattices / 1.5：
Exercises / 1.6：
Basics of Fuzzy Sets / 2：
Fuzzy Sets and Their Set- Theoretic Operations / 2.1：
General Fuzzy Logic Connectives / 2.2：
Fuzzy Negations / 2.2.1：
Triangular Norms and Conorms / 2.2.2：
Fuzzy Implications / 2.2.3：
Fuzzy Equivalencies / 2.2.4：
Decomposition of a Fuzzy Set / 2.3：
Mathematical Representation of Fuzzy Sets / 2.4：
L-Fuzzy Sets / 2.5：
Pseudo-complements / 2.5.1：
L-Fuzzy Sets and Their Set-Theoretic Operations / 2.5.2：
Decomposition of an L-Fuzzy Set / 2.5.3：
Mathematical Representation of L-Fuzzy Sets / 2.5.4：
Fuzzy Pattern Recognition / 2.6：
Type I Fuzzy Pattern Recognition / 2.6.1：
Type II Fuzzy Pattern Recognition / 2.6.2：
Fuzzy Relations / 2.7：
Basic Concepts of Fuzzy Relations / 3.1：
Compositions of Fuzzy Relations / 3.2：
Round Composition of Fuzzy Relations / 3.2.1：
Subcomposition, Supercomposition and Square Composition of Fuzzy Relations / 3.2.2：
Fuzzy Equivalence Relations / 3.3：
Closures / 3.4：
The Concept of a Closure / 3.4.1：
The Transitive Closure of a Fuzzy Relation / 3.4.2：
Fuzzy Tolerance Relations / 3.5：
Other Special Fuzzy Relations / 3.6：
Crisp Negative Transitivity, Semitransitivity, and Ferrers Property / 3.6.1：
Negative S-Transitivity, T-S-Semitransitivity, and T-S-Ferrers Property / 3.6.2：
Consistency, Weak Transitivity and Acyclicity / 3.6.3：
Fuzzy Relation Equations / 3.7：
Some Applications of Fuzzy Relations / 3.8：
Fuzzy Clustering Analysis / 3.8.1：
An Application to Information Retrieval / 3.8.2：
An Application to Multiple Attribute Decision Making Analysis / 3.8.3：
Extension Principle and Fuzzy Numbers / 3.9：
Unary Extension Principle / 4.1：
n-Ary Extension Principle / 4.2：
Convex Fuzzy Quantities / 4.3：
Fuzzy Numbers / 4.4：
The Concept of a Fuzzy Number / 4.4.1：
Properties of Algebraic Operations on Fuzzy Numbers / 4.4.2：
Ranking of Fuzzy Numbers / 4.5：
Ranking Fuzzy Numbers by a Ranking Function / 4.5.1：
Ranking Fuzzy Numbers According to the Closeness to a Reference Set / 4.5.2：
Ranking Fuzzy Numbers Based on Pairwise Comparisons / 4.5.3：
Ranking Axioms / 4.5.4：
An Application of Fuzzy Numbers / 4.6：
A Brief Introduction to Some Pure Mathematical Topics / 4.7：
Fuzzy Measures and Fuzzy Integrals / 5.1：
Fuzzy Measures / 5.1.1：
Fuzzy Integrals / 5.1.2：
Fuzzy Algebra / 5.2：
Fuzzy Subgroups / 5.2.1：
Normal Fuzzy Subgroups / 5.2.2：
Fuzzy Subrings / 5.2.3：
Fuzzy Ideals / 5.2.4：
Fuzzy Topology / 5.3：
Definitions / 5.3.1：
Characterization of a Fuzzy Topology in Terms of Preassigned Operations / 5.3.2：
Characterization of a Fuzzy Topology in Terms of Closed Sets / 5.3.3：
Characterization of a Fuzzy Topology Using the Interior Operator / 5.3.4：
Characterization of a Fuzzy Topology by Means of a Closure Operator / 5.3.5：
Characterization of a Fuzzy Topology by Means of Neighborhood Systems / 5.3.6：
Normality in Fuzzy Topological Spaces / 5.3.7：
Some Examples of Fuzzy Topological Spaces / 5.3.8：
Fuzzy Inference and Fuzzy Control / 5.4：
Linguistic Variables and Hedges / 6.1：
Fuzzy Propositions and IF-THEN Rules / 6.2：
Fuzzy Inference Rules / 6.3：
The Calculation of Inference Results / 6.4：
Fuzzification and Defuzzification / 6.5：
The Principle of Fuzzy Control / 6.6：
References / 6.7：
Index
Preliminaries / 1：
Sets / 1.1：
Relations / 1.2：
7.

図書
図書
7. Fuzzy implications
Michał Baczyński ; Balasubramaniam Jayaram
出版情報: Berlin : Springer, c2008  xviii, 310 p. ; 24 cm
シリーズ名: Studies in fuzziness and soft computing ; 231
所蔵情報: loading…
目次情報: 続きを見る
Preface
Notations and Some Preliminaries
An Introduction to Fuzzy Implications / 1：
Definition and Basic Examples / 1.1：
Continuity of Fuzzy Implications / 1.2：
Basic Properties of Fuzzy Implications / 1.3：
Negations from Fuzzy Implications / 1.4：
Fuzzy Negations / 1.4.1：
Natural Negations of Fuzzy Implications / 1.4.2：
Laws of Contraposition / 1.5：
Reciprocal Fuzzy Implications / 1.6：
Bibliographical Remarks / 1.7：
Analytical Study of Fuzzy Implications / Part I：
Fuzzy Implications from Fuzzy Logic Operations / 2：
Fuzzy Conjunctions: Triangular Norms / 2.1：
Fuzzy Disjunctions: Triangular Conorms / 2.2：
Relationships between Negations, T-Norms and T-Conorms / 2.3：
Natural Negations of T-Norms and T-Conorms / 2.3.1：
Laws of Excluded Middle and Contradiction / 2.3.2：
De Morgan Triples / 2.3.3：
(S,N)-Implications and S-Implications / 2.4：
Motivation, Definition and Examples / 2.4.1：
Characterizations of (S,N)-Implications / 2.4.2：
(S,N)-Implications and the Identity Principle / 2.4.3：
(S,N)-Implications and the Ordering Property / 2.4.4：
Intersections between Subfamilies of (S,N)-Implications / 2.4.5：
R-Implications / 2.5：
Properties of R-Implications / 2.5.1：
Characterizations and Representations of R-Implications / 2.5.3：
R-Implications and Laws of Contraposition / 2.5.4：
Intersections between Subfamilies of R-Implications / 2.5.5：
QL-Implications / 2.6：
Definition, Examples and Basic Properties / 2.6.1：
QL-Implications and the Exchange Principle / 2.6.2：
QL-Implications and the Identity Principle / 2.6.3：
QL-Implications and the Ordering Property / 2.6.4：
QL-Implications and the Law of Contraposition / 2.6.5：
Fuzzy Implications from Generator Functions / 2.7：
f-Generated Implications / 3.1：
Definition and Examples / 3.1.1：
Properties of f-Implications / 3.1.2：
g-Generated Implications / 3.2：
Properties of g-Implications / 3.2.1：
Intersections between Families of Fuzzy Implications / 3.3：
Intersections between (S,N)- and R-Implications / 4.1：
Intersections between (S,N)- and QL-Implications / 4.2：
Intersections between R- and QL-Implications / 4.3：
Intersections between Yager's f- and g-Implications / 4.4：
Intersections between Yager's and (S,N)-Implications / 4.5：
Intersections between Yager's and R-Implications / 4.6：
Intersections between Yager's and QL-Implications / 4.7：
Fuzzy Implications from Uninorms / 4.8：
Uninorms / 5.1：
Definitions and Examples / 5.1.1：
Pseudo-continuous Uninorms / 5.1.2：
Idempotent Uninorms / 5.1.3：
Representable Uninorms / 5.1.4：
Natural Negations of Fuzzy Implications - Revisited / 5.2：
(U,N)-Implications / 5.3：
Definition and Basic Properties / 5.3.1：
Characterizations of (U,N)-Implications / 5.3.2：
RU-Implications / 5.4：
RU-Implications from Pseudo-continuous Uninorms / 5.4.1：
RU-Implications from Representable Uninorms / 5.4.3：
RU-Implications from Idempotent Uninorms / 5.4.4：
Intersections between (U,N)- and RU-Implications / 5.5：
Intersection between I[subscript U,N] and I[subscript U subscript M] / 5.5.1：
Intersection between I[subscript U,N] and I[subscript U subscript R] / 5.5.2：
Intersection between I[subscript U,N] and I[subscript U subscript I] / 5.5.3：
Intersection between I[subscript U subscript M] and I[subscript U subscript R] / 5.5.4：
Intersection between I[subscript U subscript M] and I[subscript U subscript I] / 5.5.5：
Intersection between I[subscript U subscript R] and I[subscript U subscript I] / 5.5.6：
Algebraic Study of Fuzzy Implications / 5.6：
Algebraic Structures of Fuzzy Implications / 6：
Lattice of Fuzzy Implications / 6.1：
Convex Classes of Fuzzy Implications / 6.2：
Conjugacy Classes of Fuzzy Implications / 6.3：
Semigroups of Fuzzy Implications / 6.4：
Composition of Fuzzy Implications / 6.4.1：
Fuzzy Implications and Some Functional Equations / 6.4.2：
Contrapositive Symmetrization of Fuzzy Implications / 7.1：
Upper and Lower Contrapositivisations / 7.1.1：
Medium Contrapositivisation / 7.1.2：
Distributivity of Fuzzy Implications / 7.2：
On the Equation I(S(x, y), z) = T(I(x, z), I(y, z)) / 7.2.1：
On the Equation I(T(x, y), z) = S(I(x, z), I(y, z)) / 7.2.2：
On the Equation I(x, T[subscript 1](y, z)) = T[subscript 2](I(x, y), I(x, z)) / 7.2.3：
On the Equation I(x, S[subscript 1](y, z)) = S[subscript 2](I(x, y), I(x, z)) / 7.2.4：
The Law of Importation / 7.3：
(S,N)-Implications and the Law of Importation / 7.3.1：
R-Implications and the Law of Importation / 7.3.2：
QL-Implications and the Law of Importation / 7.3.3：
f- and g-Implications and the Law of Importation / 7.3.4：
Fuzzy Implications and T-Conditionality / 7.4：
(S,N)-Implications and T-Conditionality / 7.4.1：
R-Implications and T-Conditionality / 7.4.2：
QL-Implications and T-Conditionality / 7.4.3：
Characterization through Functional Equations / 7.5：
Applicational Study of Fuzzy Implications / 7.6：
Fuzzy Implications in Approximate Reasoning / 8：
Approximate Reasoning / 8.1：
Classical Implication in Inference Schemas / 8.1.1：
Fuzzy Implication in Inference Schemas / 8.1.2：
Fuzzy IF-THEN Rules / 8.1.3：
Possibility Distribution / 8.2.1：
Fuzzy Statements / 8.2.2：
Inference Schemes in Approximate Reasoning / 8.2.3：
Generalized Modus Ponens (GMP) / 8.3.1：
Compositional Rule of Inference (CRI) / 8.3.2：
Inference in CRI with Multiple Rules / 8.3.3：
Similarity Based Reasoning (SBR) / 8.3.4：
Effectiveness of Inference Schemes in AR / 8.4：
GMP Rules and AR / 8.4.1：
Function Approximation and AR / 8.4.2：
Efficiency of Inference Schemes in AR / 8.5：
Modification of the CRI Inference Algorithm / 8.5.1：
Transformation of the Structure of the Rules / 8.5.2：
Appendix / 8.6：
Some Results on Real Functions / A：
References
List of Figures
List of Tables
Index
Preface
Notations and Some Preliminaries
An Introduction to Fuzzy Implications / 1：
8.

図書
図書
8. Reviews of plasma physics (v. 1 - v. 24)
authorized translation from the Russian by Herbert Lashinsky ; edited by M.A. Leontovich
出版情報: New York : Consultants Bureau, 1965-  v. ; 24 cm
所蔵情報: loading…
目次情報: 続きを見る
Cooperative Effects in Plasmas / B.B. KadomtsevPart 1：
Preliminaries / 1：
Nonlinear Waves / 2：
Waves and Particles / 3：
Plasma in a Magnetic Field / 4：
Linear Waves / 5：
Relativistic Interaction of Laser Pulse With Plasmas / S.V. Bulanov ; F. Califano ; G.I. Dudnikova ; T.Zh. Esirkepov ; I.N. Inovenkov ; F.F. Kamenets ; T.V. Liseikina ; M. Lontano ; K. Mima ; N. M. Naumova ; K. Nishihara ; F. Pegoraro ; H. Ruhl ; A.S. Sakharov ; Y. Sentoku ; V.A. Vshivkov ; V.V. ZhakhovskiiPart 2：
Introduction
Relativistically strong electromagnetic waves in underdense plasmas
Acceleration of charged particles and photons
Filamentation of the laser light and magnetic interaction of filaments and electromagnetic radiation
Relativistic solitons
Interactions of an ultrashort, relativistically strong, laser pulse with an overdense plasma
Nonlinear interactions of laser pulses with a foil / 6：
Coulomb explosion of a cluster irradiated by a high intensity laser pulse / 7：
Conclusions / 8：
References
Theoretical Principles of the Plasma-Equilibrium Control in Stellarators / V. D. Pustovitov
History of the problem and a general review of the theory / 1.：
The first problems of tokamaks and stellarators / 1.1.：
The problem of high [beta] / 1.2.：
Development of the MHD theory of stellarators / 1.3.：
High [beta] and the problem of plasmaequilibrium control / 1.4.：
Free-boundary plasma equilibrium / 1.5.：
Plasma-shape control in stellarators / 1.6.：
General equations of the theory of plasma equilibrium in conventional stellarators / 2.：
Stellarator approximation and the magnetic differential equation / 2.1.：
Real and averaged magnetic surfaces / 2.2.：
Integral quantities / 2.3.：
Currents in equilibrium configurations / 2.4.：
Longitudinal current in a stellarator / 2.5.：
Two-dimensional equation of plasma equilibrium in stellarators / 2.6.：
Analytical models / 3.：
Two-dimensional model of a stellarator / 3.1.：
Minimal set of parameters / 3.2.：
Description of the inner part of the plasma / 3.3.：
Effect of satellite harmonics on the stellarator configuration / 3.4.：
Control of plasma equilibrium using a vertical magnetic field / 4.：
Boundary conditions in equilibrium problems / 4.1.：
Reduction of the boundary conditions / 4.2.：
Effect of a vertical field on the plasmacolumn position in stellarators / 4.3.：
Suppression of the Pfirsch-Schluter current in conventional stellarators / 4.4.：
Integral independence on [beta] and "overcompensation" / 4.5.：
The influence of a quadrupole field on the stellarator configuration / 5.：
Control of the vacuum stellarator configuration using a quadrupole field / 5.1.：
Doublet-like stellarator configurations / 5.2.：
Control of the rotational-transform profile with the help of the quadrupole field / 5.3.：
Elongation of the plasma column as a means of increasing [beta][subscript eq] in stellarators / 5.4.：
List of main symbols
Fundamentals of Stationary Plasma Thruster Theory / A. I. Morozov ; V. V. Savelyev
General picture of processes in SPTs
Principal scheme of an SPT
Specifics of physical processes in SPTs
Quasi-autonomous functional units of SPTs
General system of equations and boundary conditions for SPT processes
Magnetic and electric fields in SPTs
Magnetic fields in SPTs
"Equipotentialization" of the magnetic force lines. Magnetic drift surfaces
The "loading" of magnetic force lines
Plasma electric field for the quasi-Maxwellian electron component
Remarks
Electron kinetics in the SPT channel
Characteristics of particle collisions with each other and with the surfaces
Electron distribution functions in the SPT channel
Debye layers on the SPT channel walls
The near-wall conductivity (NWC)
UHF-oscillations in the SPT channel / 3.5.：
Some conclusions / 3.6.：
Erosion of insulators in SPTs
The role and form of insulator erosion
Ion sputtering
Mathematical modeling of the anomalous erosion
Heavy particle dynamics in the SPT channel
Dynamics of single heavy particles
A kinetic description of ionizing heavy particles
Similarity criteria for discharges in SPT
The "inverse" problem of heavy particle dynamics
An analysis of processes using the emerging flux characteristics / 5.5.：
Estimate of energetic balance components in the SPT-ATON / 5.6.：
Low-frequency oscillations in SPTs / 6.：
Experimental data on LF-oscillations in the SPT channel / 6.1.：
Linear oscillations in a one-dimensional flux model without ionization / 6.2.：
One-dimensional self-consistent models for plasma flow in an SPT channel / 7.：
Modeling an SPT in the one-dimensional hydrodynamic approximation / 7.1.：
The results of calculations in the hydrodynamic model / 7.2.：
Dynamics of oscillations / 7.3.：
A hybrid model for the plasma flow in an SPT / 7.4.：
SPTs in real conditions / 8.：
The particle influx from the VC into the SPT / 8.1.：
Preventing particle influx from the VC / 8.2.：
Supersynchronization phenomenon / 8.3.：
Appendix
The necessity of electric propulsion thrusters / A.：
Preface
Mechanisma of Transverse Conductivity and Generation of Self-Consistent Electric Fields in Strongly Ionized Magnetized Plasma / V. Rozhansky
Conductivity Tensor in Partially Ionized Plasma / 1.1：
Main Mechanisms of Perpendicular Conductivity in Fully Ionized Plasma: Currents Caused by Viscosity, Inertia, Collisions with Neutrals, and [down triangle, open]B, and Mass-Loading Currents / 1.3：
Inertia Currents / 1.3.1：
Currents Caused by Ion-Neutral Collisions / 1.3.2：
Diamagnetic Currents / 1.3.3：
Viscosity-Driven Currents / 1.3.4：
Mass-Loading Current / 1.3.5：
Inertial (Polarization) and [down triangle, open]B Currents. Acceleration of Plasma Clouds in an Inhomogeneous Magnetic Field / 1.4：
Alfven Conductivity / 1.5：
Perpendicular Viscosity, Radial Current, and Radial Electric Field in an Infinite Cylinder / 1.6：
Current Systems in Front of a Biased Electrode (Flush-Mounted Probe) and Spot of Emission / 1.7：
Viscosity-Driven Perpendicular Currents / 1.7.1：
Currents Driven by Ion-Neutral Collisions / 1.7.2：
General Situation / 1.7.3：
Spot of Emission / 1.7.5：
Currents in the Vicinity of a Biased Electrode That is Smaller Than the Ion Gyroradius / 1.8：
Neoclassical Perpendicular Conductivity in a Tokamak / 1.9：
Steady State Current / 1.9.1：
Time-Dependent Current / 1.9.2：
Transverse Conductivity in a Reversed Field Pinch / 1.10：
Modeling of Electric Field and Currents in the Tokamak Edge Plasma / 1.11：
Mechanisms of Anomalous Perpendicular Viscosity and Viscosity-Driven Currents / 1.12：
Transverse Conductivity in a Stochastic Magnetic Field / 1.13：
Nonstochastic Magnetic Field / 1.13.1：
Stochastic Magnetic Field / 1.13.2：
Electric Fields Generated in the Shielding Layer between Hot Plasma and a Solid State / 1.14：
Correlations and Anomalous Transport Models / O.G. Bakunin
Turbulent Diffusion and Transport / 2.1：
The Correlation Function and the Taylor Diffusivity / 2.2.1：
The Richardson Law / 2.2.2：
The Davydov Model of Turbulent Diffusion / 2.2.3：
The Batchelor Approximation for the Diffusion Coefficient / 2.2.4：
Nonlocal Effects and Diffusion Equations / 2.3：
The Functional Equation for Random Walks / 2.3.1：
Nonlocality and the Levy Distribution / 2.3.2：
The Monin Fractional Differential Equation / 2.3.3：
The Corrsin Conjecture / 2.4：
The Corrsin Independence Hypothesis / 2.4.1：
The Simplified Corrsin Conjecture / 2.4.2：
The Correlation Function and Scalings / 2.4.3：
Effects of Seed Diffusivity / 2.5：
Seed Diffusivity and Correlations / 2.5.1：
"Returns" and Correlations / 2.5.2：
The Stochastic Magnetic Field and Scalings / 2.5.3：
The Howells Result / 2.5.4：
The Diffusive Tracer Equation and Averaging / 2.6：
The Taylor Shear Flow Model / 2.6.1：
Generalization of the Taylor Model / 2.6.2：
The Zeldovich Flow and the Kubo Number / 2.6.3：
Advection and Zeldovich Scaling / 2.6.4：
The System of Random Shear Flows / 2.7：
The Dreizin-Dykhne Superdiffusion Regime / 2.7.1：
The Matheron-de Marsily Model / 2.7.2：
The "Manhattan Grid" Flow and Transport / 2.7.3：
The Quasi-Linear Approximation / 2.8：
Quasi-Linear Equations / 2.8.1：
Short-Range and Long-Range Correlations / 2.8.2：
The Telegraph Equation / 2.8.3：
Magnetic Diffusivity and the Kubo Number / 2.8.4：
The Diffusive Renormalization / 2.9：
The Dupree Approximation / 2.9.1：
The Dupree Theory Revisited / 2.9.2：
The Taylor-McNamara Correlation Function / 2.9.3：
The Kadomtsev-Pogutse Renormalization and the Stochastic Magnetic Field / 2.9.4：
Anomalous Transport and Convective Cells / 2.10：
Bohm Scaling and Electric Field Fluctuations / 2.10.1：
The Bohm Regime and Correlations / 2.10.2：
Convective Cells and Transport / 2.10.3：
Complex Structures and Convective Transport / 2.10.4：
Stochastic Instability and Transport / 2.11：
Stochastic Instability and Correlations / 2.11.1：
The Rechester-Rosenbluth Model / 2.11.2：
Collisional Effects and the Stix Formula / 2.11.3：
The Quasi-Isotropic Stochastic Magnetic Field and Transport / 2.11.4：
Quasi-Linear Scaling for the Stochastic Instability Increment / 2.11.5：
Fractal Conceptions and Turbulence / 2.12：
Fractality and Transport / 2.12.1：
The Richardson Law and Fractality / 2.12.2：
Intermittency and the Kolmogorov Law / 2.12.3：
Percolation and Scalings / 2.13：
Continuum Percolation and Transport / 2.13.1：
Renormalization and Percolation / 2.13.2：
Graded Percolation / 2.13.3：
Percolation and Turbulent Transport Scalings / 2.14：
Random Steady Flows and Seed Diffusivity / 2.14.1：
The Spatial Hierarchy of Scales and Stochastic Instability / 2.14.2：
Low Frequency Regimes / 2.14.3：
The Temporal Hierarchy of Scales and Correlations / 2.15：
The Spatial and Temporal Hierarchy of Scales / 2.15.1：
The Isichenko Intermediate Regime / 2.15.2：
Dissipation and Percolation Transport / 2.15.3：
The Stochastic Magnetic Field and Percolation Transport / 2.16：
Percolation and the Kadomtsev-Pogutse Scaling / 2.16.1：
Percolation Renormalization and the Stochastic Instability Increment / 2.16.3：
Percolation in Drift Flows / 2.17：
Graded Percolation and Drift Flows / 2.17.1：
Low Frequency Regimes and Drift Effects / 2.17.2：
Compressibility and Percolation / 2.17.3：
Multiscale Flows / 2.18：
The Nested Hierarchy of Scales and Drift Effects / 2.18.1：
The Brownian Landscape and Percolation / 2.18.2：
Correlations and Transport Scalings / 2.18.3：
The Diffusive Approximation and the Multiscale Model / 2.18.4：
Stochastic Instability and Time Scales / 2.18.5：
Isotropic and Anisotropic Turbulent Energy Spectra / 2.18.6：
The Multiscale Model of Transport in a Tangled Magnetic Field / 2.18.7：
Subdiffusion and Traps / 2.19：
The Balagurov and Vaks Model of Diffusion with Traps / 2.19.1：
Subdiffusion and Fractality / 2.19.2：
Comb Structures and Transport / 2.19.3：
Continuous Time Random Walks / 2.20：
The Montroll and Weiss Approach and Memory Effects / 2.20.1：
Fractional Differential Equations / 2.20.2：
The Taylor Definition and Memory Effects / 2.20.3：
Fractional Differential Equations and Scalings / 2.21：
The Klafter, Blumen, and Shlesinger Approximation / 2.21.1：
The Stochastic Magnetic Field and Balescu Approach / 2.21.2：
Longitudinal Correlations and the Diffusive Approximation / 2.21.3：
Vortex Structures and Trapping / 2.21.4：
Correlations and Trapping / 2.21.5：
Correlation and Phase-Space / 2.22：
The Corrsin Conjecture and Phase-Space / 2.22.1：
The Hamiltonian Nature of the Universal Hurst Exponent / 2.22.2：
The One-Flight Model and Transport / 2.22.3：
Correlations and Nonlocal Velocity Distribution / 2.22.4：
The Arrhenius Law and Phase-Space Distribution / 2.22.5：
Conclusion / 2.23：
Acknowledgements
Cooperative Effects in Plasmas / B.B. KadomtsevPart 1：
Preliminaries / 1：
Nonlinear Waves / 2：
9.

図書
図書
9. Modeling uncertainty with fuzzy logic : with recent theory and applications
Asli Celikyilmaz and I. Burhan Türksen
出版情報: Berlin : Springer, c2009  ix, 400 p. ; 25 cm
シリーズ名: Studies in fuzziness and soft computing ; 240
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1：
Motivation / 1.1：
Contents of the Book / 1.2：
Outline of the Book / 1.3：
Fuzzy Sets and Systems / 2：
Type-1 Fuzzy Sets and Fuzzy Logic / 2.1：
Characteristics of Fuzzy Sets / 2.2.1：
Operations on Fuzzy Sets / 2.2.2：
Fuzzy Logic / 2.3：
Structure of Classical Logic Theory / 2.3.1：
Relation of Set and Logic Theory / 2.3.2：
Structure of Fuzzy Logic / 2.3.3：
Approximate Reasoning / 2.3.4：
Fuzzy Relations / 2.4：
Operations on Fuzzy Relations / 2.4.1：
Extension Principle / 2.4.2：
Type-2 Fuzzy Sets / 2.5：
Interval Valued Type-2 Fuzzy Sets / 2.5.1：
Type-2 Fuzzy Set Operations / 2.5.3：
Fuzzy Functions / 2.6：
Fuzzy Systems / 2.7：
Extensions of Takagi-Sugeno Fuzzy Inference Systems / 2.8：
Adaptive-Network-Based Fuzzy Inference System (ANFIS) / 2.8.1：
Dynamically Evolving Neuro-Fuzzy Inference Method (DENFIS) / 2.8.2：
Genetic Fuzzy Systems (GFS) / 2.8.3：
Summary / 2.9：
Improved Fuzzy Clustering / 3：
Fuzzy Clustering Algorithms / 3.1：
Fuzzy C-Means Clustering Algorithm / 3.2.1：
Classification of Objective Based Fuzzy Clustering Algorithms / 3.2.2：
Fuzzy C-Regression Model (FCRM) Clustering Algorithm / 3.2.3：
Variations of Combined Fuzzy Clustering Algorithms / 3.2.4：
Improved Fuzzy Clustering Algorithm (IFC) / 3.3：
Improved Fuzzy Clustering Algorithm for Regression Models (IFC) / 3.3.1：
Improved Fuzzy Clustering Algorithm for Classification Models (IFC-C) / 3.3.3：
Justification of Membership Values of the IFC Algorithm / 3.3.4：
Two New Cluster Validity Indices for IFC and IFC-C / 3.4：
Overview of Well-Known Cluster Validity Indices / 3.4.1：
The New Cluster Validity Indices / 3.4.2：
Simulation Experiments [Celikyilmaz and Turksen, 2007i;2008c] / 3.4.3：
Discussions on Performances of New Cluster Validity Indices Using Simulation Experiments / 3.4.4：
Fuzzy Functions Approach / 3.5：
Proposed Type-1 Fuzzy Functions Approach Using FCM - T1FF / 4.1：
Structure Identification of FF for Regression Models (T1FF) / 4.3.1：
Structure Identification of the Fuzzy Functions for Classification Models (T1FF-C) / 4.3.2：
Inference Mechanism of T1FF for Regression Models / 4.3.3：
Inference Mechanism of T1FF for Classification Models / 4.3.4：
Proposed Type-1 Improved Fuzzy Functions with IFC - T1IFF / 4.4：
Structure Identification of T1IFF for Regression Models / 4.4.1：
Structure Identification of T1IFF-C for Classification Models / 4.4.2：
Inference Mechanism of T1IFF for Regression Problems / 4.4.3：
Inference with T1IFF-C for Classification Problems / 4.4.4：
Proposed Evolutionary Type-1 Improved Fuzzy Function Systems / 4.5：
Genetic Learning Process: Genetic Tuning of Improved Membership Functions and Improved Fuzzy Functions / 4.5.1：
Inference Method for ET1IFF and ET1IFF-C / 4.5.2：
Reduction of Structure Identification Steps of T1IFF Using the Proposed ET1IFF Method / 4.5.3：
Modeling Uncertainty with Improved Fuzzy Functions / 4.6：
Uncertainty / 5.1：
Conventional Type-2 Fuzzy Systems / 5.3：
Generalized Type-2 Fuzzy Rule Bases Systems (GT2FRB) / 5.3.1：
Interval Valued Type-2 Fuzzy Rule Bases Systems (IT2FRB) / 5.3.2：
Most Common Type-Reduction Methods / 5.3.3：
Discrete Interval Type-2 Fuzzy Rule Bases (DIT2FRB) / 5.3.4：
Discrete Interval Type-2 Improved Fuzzy Functions / 5.4：
Background of Type-2 Improved Fuzzy Functions Approaches / 5.4.1：
Discrete Interval Type-2 Improved Fuzzy Functions System (DIT2IFF) / 5.4.2：
The Advantages of Uncertainty Modeling / 5.5：
Discrete Interval Type-2 Improved Fuzzy Functions with Evolutionary Algrithms / 5.6：
Architecture of the Evolutionary Type-2 Improved Fuzzy Functions / 5.6.1：
Reduction of Structure Identification Steps of DIT2IFF Using New EDIT2IFF Method / 5.6.3：
Experiments / 5.7：
Experimental Setup / 6.1：
Overview of Experiments / 6.1.1：
Three-Way Sub-sampling Cross Validation Method / 6.1.2：
Measuring Models' Prediction Performance / 6.1.3：
Performance Evaluations of Regression Experiments / 6.1.3.1：
Performance Evaluations of Classification Experiments / 6.1.3.2：
Parameters of Benchmark Algorithms / 6.2：
Support Vector Machines (SVM) / 6.2.1：
Artificial Neural Networks (NN) / 6.2.2：
Discrete Interval Valued Type-2 Fuzzy Rule Base (DIT2FRB) / 6.2.3：
Genetic Fuzzy System (GFS) / 6.2.6：
Logistic Regression, LR, Fuzzy K-Nearest Neighbor, FKNN / 6.2.7：
Parameters of Proposed Fuzzy Functions Algorithms / 6.3：
Fuzzy Functions Methods / 6.3.1：
Imporoved Fuzzy Functions Methods / 6.3.2：
Analysis of Experiments - Regression Domain / 6.4：
Friedman's Artificial Domain / 6.4.1：
Auto-mileage Dataset / 6.4.2：
Desulphurization Process Dataset / 6.4.3：
Stock Price Analysis / 6.4.4：
Proposed Fuzzy Cluster Validity Index Analysis for Regression / 6.4.5：
Analysis of Experiments - Classification (Pattern Recognition) Domains / 6.5：
Classification Datasets from UCI Repository / 6.5.1：
Classification Dataset from StatLib / 6.5.2：
Results from Classification Datasets / 6.5.3：
Proposed Fuzzy Cluster Validity Index Analysis for Classification / 6.5.4：
Performance Comparison Based on Elapsed Times / 6.5.5：
Overall Discussions on Experiments / 6.6：
Overall Comparison of System Modeling Methods on Regression Datasets / 6.6.1：
Overall Comparison of System Modeling Methods on Classification Datasets / 6.6.2：
Summary of Results and Discussions / 6.7：
Conclusions and Future Work / 7：
General Conclusions / 7.1：
Future Work / 7.2：
References
Appendix
Set and Logic Theory - Additional Information / A.1：
Fuzzy Relations (Composition) - An Example / A.2：
Proof of Fuzzy c-Means Clustering Algorithm / B.1：
Proof of Improved Fuzzy Clustering Algorithm / B.2：
Artificial Neural Networks ANNs) / C.1：
Support Vector Machines / C.2：
Genetic Algorithms / C.3：
Multiple Linear Regression Algorithms with Least Squares Estimation / C.4：
Logistic Regression / C.5：
Fuzzy K-Nearest Neighbor Approach / C.6：
T-Test Formula / D.1：
Friedman's Artificial Dataset: Summary of Results / D.2：
Auto-mileage Dataset: Summary of Results / D.3：
Desulphurization Dataset: Summary of Results / D.4：
Stock Price Datasets: Summary of Results / D.5：
Classification Datasets: Summary of Results / D.6：
Cluster Validity Index Graphs / D.7：
Classification Datasets - ROC Graphs / D.8：
Introduction / 1：
Motivation / 1.1：
Contents of the Book / 1.2：
10.

図書
図書
10. Random surfaces and 2D quantum gravity : proceedings of the Workshop on Random Surfaces and 2D Quantum Gravity, Barcelona, Spain, 10-14 June 1991
edited by J. Ambjorn ... [et al.]
出版情報: Amsterdam : North-Hollad, 1992  vii, 217 p. ; 27 cm
シリーズ名: Nuclear physics. B, Proceedings, supplements ; vol. 25A
所蔵情報: loading…
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