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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. 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
3.

図書
図書
3. 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：
4.

図書
図書
4. 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：
5.

図書
図書
5. 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
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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：
6.

図書
図書
6. 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
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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：
7.

図書
図書
7. 2nd European symposium on enzymes in grain processing : ESEPG-2 : Helsinki, Finland, 8-10 December, 1999
edited by Taina Simoinen and Maija Tenkanen ; Organised by VTT Biotechnology, TNO Nutrition and Food Research Institute
出版情報: Espoo : Valtion Teknillinen Tutkimuskeskus, c2000  340 p. ; 30 cm
シリーズ名: VTT Symposium ; 207
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8.

図書
図書
8. New frontiers in colloid science : a celebration of the career of Brian Vincent
edited by Simon Biggs, Terence Cosgrove, Peter Dowding
出版情報: Cambridge, UK : Royal Society of Chemistry, c2008  xii, 199 p. ; 24 cm
シリーズ名: Special publication / Royal Society of Chemistry ; 314
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目次情報: 続きを見る
A Journey Through Colloid Science / Brian VincentChapter 1：
Early Days / 1.1：
Novel Colloidal Systems / 1.2：
Particle Synthesis / 1.2.1：
Particles with Terminally Grafted Polymer Chains / 1.2.2：
Electrically Conducting Particles / 1.2.3：
Microemulsions Based on Block or Graft Copolymers / 1.2.4：
Monodisperse Liquid Droplets / 1.2.5：
Liquid Core-Solid Shell Particles / 1.2.6：
Microgel Particles / 1.2.7：
Particle Interactions / 1.3：
Electrostatic Interactions / 1.3.1：
Polymers at Interfaces and Steric Interactions / 1.3.2：
Depletion Interactions / 1.3.3：
Interactions in Mixed Particle Systems / 1.3.4：
Deposition of Particles on Flat Surfaces / 1.3.5：
Concluding Remarks / 1.4：
References
Synthesis of Poly(N-isopropylacrylamide) Microgel Particles Containing Gold Nanoshell Cores with Potential for Triggered De-swelling / Paul Luckham ; Carlo Strazza ; Pierre Bussierre ; Paulo Nassari ; Neil PatelChapter 2：
Introduction / 2.1：
Materials and Methods / 2.2：
Preparation of Silica Particles / 2.2.1：
Preparation of Positively Charged Silica Particles / 2.2.2：
Preparation of Gold Nanoparticles / 2.2.3：
Attachment of Gold Nanoparticles to Silica Cores / 2.2.4：
Growth of Gold Nanoshells / 2.2.5：
Synthesis of Hydrogel-coated Gold or Gold Nanoshells / 2.2.6：
Results and Discussion / 2.3：
Adsorption of Gold Nanoparticles onto Positively Charged Silica Particles / 2.3.1：
Polymer Chemistry, Hypervelocity Physics and the Cassini Space Mission / Steven P. Armes2.4：
Organic Conducting Polymers / 3.1：
Synthesis and Properties of Polypyrrole / 3.3：
Hypervelocity Acceleration Experiments / 3.4：
Do Plasma Mass Spectra Originate from the Latex Core or the Shell? / 3.5：
Kinetic Energy of a Hypervelocity Impact / 3.6：
The Cassini Space Mission and the CDA Detector / 3.7：
Four Classes of Micrometeorites / 3.8：
What is the Nature of the Volcanic Activity on Io? / 3.9：
Latex Mimics for Sulfur-rich Micrometeorites / 3.10：
Conclusions / 3.11：
Acknowledgements
From Novel Monodisperse "Silicone Oil"/Water Emulsions to Drug Delivery / Clive A. PrestidgeChapter 4：
Background / 4.1：
Interaction Forces and Deformation of PDMS Droplets / 4.2：
Rheological Behaviour of Concentrated Emulsions of PDMS Droplets / 4.4：
Polymers at Silicone Droplet Interfaces / 4.5：
Nanoparticles at PDMS Droplets / 4.6：
Towards a Drug Delivery System / 4.7：
Polymers and Surfactants at Interfaces: Colloidal Lego for Nanotechnology / Simon Biggs4.8：
Self-assembly of Block Copolymers and the Formation of 'Smart Nanoparticles' / 5.1：
Adsorption of Block Copolymer Micelles at the Aqueous-Solid Interface / 5.3：
Responsive and Functional Surfaces / 5.4：
Multilayer Coatings, Particles and Capsules / 5.5：
Polymer Depletion: Recent Progress for Polymer/Colloid Phase Diagrams / Gerard Fleer5.6：
Theory / 6.1：
System and System Parameters / 6.2.1：
Thermodynamics / 6.2.2：
Some Illustrations / 6.2.3：
Results / 6.3：
Nanobubbles, Dissolved Gas, Boundary Layers and Related Mysterious Effects in Colloid Stability / John Ralston6.4：
Stability Ratio as a Function of Dissolved Gas and Hydrophobicity / 7.1：
Colloid Stability Analysis / 7.2.2：
TMAFM Imaging / 7.2.3：
Images of Bubble Coalescence / 7.2.4：
Nature of Domains / 7.2.5：
Bubble Formation on Solid-Water Interfaces / 7.2.6：
Contact Angles and Line Tension / 7.2.7：
Kinetics of CO[subscript 2] Gas Adsorption Using a QCM / 7.2.8：
Acknowledgement / 7.3：
Heteroflocculation Studies of Colloidal Poly(N-isopropylacrylamide) Microgels with Polystyrene Latex Particles: Effect of Particle Size, Temperature and Surface Charge / Martin J. Snowden ; Louise H. Gracia ; Hani NurChapter 8：
Introduction and Background / 8.1：
Heteroflocculation Studies / 8.1.1：
Experimental / 8.2：
Microgel Synthesis / 8.2.1：
Dry Weight Analysis / 8.2.2：
Preparation of Anionic Polystyrene Latex and Cationic Microgel Mixed Dispersions / 8.2.3：
Dynamic Light Scattering / 8.2.4：
Electrophoretic Mobility and Zeta Potential Measurements / 8.2.5：
Turbidimetric Analysis / 8.2.6：
Scanning Electron Microscopy (SEM) / 8.2.7：
Scanning Electron Microscopy / 8.3：
Characterisation of Anionic PS Latex Particles / 8.3.2：
Heteroflocculation of Microgel/Latex Mixtures / 8.3.3：
Surface Modification, Encapsulation and Coating: A Career Built on Graft / David Fairhurst8.4：
The Early Years: Colloid Science, Zeta Potential and Polymers / 9.1：
The Dark Side: Black Boxes, Sunscreens and Hope-in-a-Bottle / 9.2：
Back to the Future: Colloid Science, Vaccines, Microbicides and HIV/AIDS / 9.3：
Subject Index / 9.4：
A Journey Through Colloid Science / Brian VincentChapter 1：
Early Days / 1.1：
Novel Colloidal Systems / 1.2：
9.

図書
図書
9. Quantitation of amino acids and amines by chromatography : methods and protocols. 1st ed.
edited by Ibolya Molnár-Perl
出版情報: Amsterdam : Elsevier B. V., c2005  xii, 655 p. ; 25 cm
シリーズ名: Journal of chromatography library ; 70
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目次情報: 続きを見る
Amino Acids / Part 1：
Gas Chromatography / 1.1：
Quantitation of Amino Acids as Chloroformates - A Return to Gas Chromatography / Petr Husek1.1.1：
Quantitation of Amino Acids by Gas-Liquid Chromatography / Charles W. Gehrke1.1.2：
Chiral separations of Amino Acids by Gas Chromatography / Ralf Patzold ; Hans Bruckner1.1.3：
High Performance Liquid Chromatography / 1.2：
HPLC of Amino Acids without Derivatization / Claire Elfakir1.2.1：
HPLC of Amino Acids as Phenylthiocarbamoyl Derivatives / Ibolya Molnar-Perl1.2.2：
HPLC of Amino Acids as o-Phthalaldehyde Derivatives / 1.2.3：
HPLC of Amino Acids as Chloroformate Derivatives / Bjorn Josefsson1.2.4：
HPLC of Amino Acids as Dansyl and Dabsyl Derivatives / Toyohide Takeuchi1.2.5：
Quantitation of Amino Acids as 6-Aminoquinolyl-N-hydroxysuccinimidyl Carbamate Derivatives / Steven A. Cohen1.2.6：
Capillary Electrophoresis/Capillary Electrochromatography / 1.3：
Determination of Underivatized Amino Acids by Capillary Electrophoresis and Capillary Electrochromatography / Christian W. Klampfl1.3.1：
Quantitation of Amino Acids as o-Phthalaldehyde derivatives / Shigeyuki Oguri1.3.2：
Capillary Electrophoresis and Capillary Electrochromatography of Amino Acids as Dansyl Derivatives / Zilin Chen1.3.3：
Amines / Part 2：
Gas Chromatographic Determination of Volatile Aliphatic and Selected Aromatic Amines, without Derivatization: Solid Phase Microextraction / Jacek Namiesnik ; Bogdan Zigmunt2.1：
Gas Chromatography of Amines as Various Derivatives / Hiroyuki Kataoka2.1.2：
HPLC of Amines as o-Phthalaldehyde Derivatives / 2.2：
Quantitation by HPLC of Amines as Dansyl Derivatives / Manuel Silva2.2.2：
HPLC of Amines as 9-Fluorenylmethyl Chloroformate Derivatives / Paul Chi Ho2.2.3：
HPLC of Biogenic Amines as 6-Aminoquinolyl-N-hydroxysuccinimidyl Derivatives / Thomas Weiss2.2.4：
Determination of Underivatized Amines by CE and CEC / 2.3：
Quantitation of Amines by Oncolumn Derivatives with o-Phthalaldehyde by CEC / 2.3.2：
Quantitation of Amino Acids and Amines, Simultaneously / 3：
Quantitation of Polyamines by Chromatography / Ynze Mengerink4：
Amino Acids / Part 1：
Gas Chromatography / 1.1：
Quantitation of Amino Acids as Chloroformates - A Return to Gas Chromatography / Petr Husek1.1.1：
10.

図書
図書
10. 2nd International Conference on Thermal Process Modelling and Computer Simulation : Nancy, France, March 31-April 2, 2003
edited by S. Denis ... [et al.]
出版情報: Les Ulis, France : EDP Sciences, 2004  xxv, 826 p. ; 25 cm
シリーズ名: Journal de physique, IV ; proceedings, v. 120
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