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1.

図書

図書
Roland Tóth
出版情報: Berlin : Springer, c2010  xxiv, 319 p. ; 24 cm
シリーズ名: Lecture notes in control and information sciences ; 403
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Introduction / 1:
New Challenges for System Identification / 1.1:
The Birth of LPV Systems / 1.2:
The Present State of LPV Identification / 1.3:
The Identification Cycle / 1.3.1:
General Picture of LPV Identification / 1.3.2:
LPV-Io Representation Based Methods / 1.3.3:
LPV-SS Representation Based Methods / 1.3.4:
Similarity to Other System Classes / 1.3.5:
Challenges and Open Problems / 1.4:
Perspectives of Orthonormal Basis Function Models / 1.5:
The Gain-Scheduling Perspective / 1.5.1:
The Global Identification Perspective / 1.5.2:
Approximation via OBF Structures / 1.5.3:
The Goal of the Book / 1.6:
Overview of Contents / 1.7:
LTI System Identification and the Role of OBFs / 2:
The Concept of Orthonormal Basis Functions / 2.1:
Signal Spaces and Inner Functions / 2.2:
General Class of Orthonormal Basis Functions / 2.3:
Takenaka-Malmquist Basis / 2.3.1:
Hambo Basis / 2.3.2:
Kautz Basis / 2.3.3:
Laguerre Basis / 2.3.4:
Pulse Basis / 2.3.5:
Orthonormal Basis Functions of MIMO Systems / 2.3.6:
Basis Functions in Continuous-Time / 2.3.7:
Modeling and Identification of LTI Systems / 2.4:
The Identification Setting / 2.4.1:
Model Structures / 2.4.2:
Properties / 2.4.3:
Linear Regression / 2.4.4:
Identification with OBFs / 2.4.5:
Pole Uncertainty of Model Estimates / 2.4.6:
Validation in the Prediction-Error Setting / 2.4.7:
The Kolmogorov n-Width Theory / 2.5:
Conclusions / 2.6:
LPV Systems and Representations / 3:
General Class of LPV Systems / 3.1:
Parameter Varying Dynamical Systems / 3.1.1:
Representations of Continuous-Time LPV Systems / 3.1.2:
Representations of Discrete-Time LPV Systems / 3.1.3:
Equivalence Classes and Relations / 3.2:
Equivalent Kernel Representations / 3.2.1:
Equivalent IO Representations / 3.2.2:
Equivalent State-Space Representations / 3.2.3:
Properties of LPV Systems and Representations / 3.3:
State-Observability and Reachability / 3.3.1:
Stability of LPV Systems / 3.3.2:
Gramians of LPV State-Space Representations / 3.3.3:
LPV Equivalence Transformations / 3.4:
State-Space Canonical Forms / 4.1:
The Observability Canonical Form / 4.1.1:
Reachability Canonical Form / 4.1.2:
Companion Canonical Forms / 4.1.3:
Transpose of SS Representations / 4.1.4:
LTI vs. LPV State Transformation / 4.1.5:
From State-Space to the Input-Output Domain / 4.2:
From the Input-Output to the State-Space Domain / 4.3:
The Idea of Recursive State-Construction / 4.3.1:
Cut-and-Shift in Continuous-Time / 4.3.2:
Cut-and-Shift in Discrete-Time / 4.3.3:
State-Maps and Polynomial Modules / 4.3.4:
State-Maps Based on Kernel Representations / 4.3.5:
State-Maps Based on Image-Representations / 4.3.6:
State-Construction in the MIMO Case / 4.3.7:
LPV Series-Expansion Representations / 4.4:
Relevance of Series-Expansion Representations / 5.1:
Impulse Response Representation of LPV Systems / 5.2:
Filter Form of LPV-IO Representations / 5.2.1:
Series Expansion in the Pulse Basis / 5.2.2:
The Impulse Response Representation / 5.2.3:
LPV Series Expansion by OBFs / 5.3:
The OBF Expansion Representation / 5.4:
Series Expansions and Gain-Scheduling / 5.5:
The Role of Gain-Scheduling / 5.5.1:
Optimality of the Basis in the Frozen Sense / 5.5.2:
Optimality of the Basis in the Global Sense / 5.5.3:
Discretization of LPV Systems / 5.6:
The Importance of Discretization / 6.1:
Discretization of LPV System Representations / 6.2:
Discretization of State-Space Representations / 6.3:
Complete Method / 6.3.1:
Approximative State-Space Discretization Methods / 6.3.2:
Discretization Errors and Performance Criteria / 6.4:
Local Discretization Errors / 6.4.1:
Global Convergence and Preservation of Stability / 6.4.2:
Guaranteeing a Desired Level of Discretization Error / 6.4.3:
Switching Effects / 6.4.4:
Properties of the Discretization Approaches / 6.5:
Discretization and Dynamic Dependence / 6.6:
Numerical Example / 6.7:
LPV Modeling of Physical Systems / 6.8:
Towards Model Structure Selection / 7.1:
General Questions of LPV Modeling / 7.2:
Modeling of Nonlinear Systems in the LPV Framework / 7.3:
First Principle Models / 7.3.1:
Linearization Based Approximation Methods / 7.3.2:
Multiple Model Design Procedures / 7.3.3:
Substitution Based Transformation Methods / 7.3.4:
Automated Model Transformation / 7.3.5:
Summary of Existing Techniques / 7.3.6:
Translation of First Principle Models to LPV Systems / 7.4:
Problem Statement / 7.4.1:
The Transformation Algorithm / 7.4.2:
Handling Non-Factorizable Terms / 7.4.3:
Properties of the Transformation Procedure / 7.4.4:
Optimal Selection of OBFs / 7.5:
Perspectives of OBFs Selection / 8.1:
Kolmogorov n-Width Optimality in the Frozen Sense / 8.2:
The Fuzzy-Kolmogorov c-Max Clustering Approach / 8.3:
The Pole Clustering Algorithm / 8.3.1:
Properties of the FKcM / 8.3.2:
Simulation Example / 8.3.3:
Robust Extension of the FKcM Approach / 8.4:
Questions of Robustness / 8.4.1:
Basic Concepts of Hyperbolic Geometry / 8.4.2:
Pole Uncertainty Regions as Hyperbolic Objects / 8.4.3:
The Robust Pole Clustering Algorithm / 8.4.4:
Properties of the Robust FKcM / 8.4.5:
LPV Identification via OBFs / 8.4.6:
Aim and Motivation of an Alternative Approach / 9.1:
OBFs Based LPV Model Structures / 9.2:
The LPV Prediction-Error Framework / 9.2.1:
The Wiener and the Hammerstein OBF Models / 9.2.2:
Properties of Wiener and Hammerstein OBF Models / 9.2.3:
OBF Models vs. Other Model Structures / 9.2.4:
Identification of W-LPV and H-LPV OBF Models / 9.2.5:
Identification with Static Dependence / 9.3:
LPV Identification with Fixed OBFs / 9.3.1:
Local Approach / 9.3.3:
Global Approach / 9.3.4:
Examples / 9.3.5:
Approximation of Dynamic Dependence / 9.4:
Feedback-Based OBF Model Structures / 9.4.1:
Properties of Wiener and Hammerstein Feedback Models / 9.4.2:
Identification by Dynamic Dependence Approximation / 9.4.3:
Example / 9.4.4:
Extension towards MIMO Systems / 9.5:
Scalar Basis Functions / 9.5.1:
Multivariable Basis Functions / 9.5.2:
Multivariable Basis Functions in the Feedback Case / 9.5.3:
General Remarks on the MIMO Extension / 9.5.4:
Proofs / 9.6:
Proofs of Chapter 3 / A.1:
The Injective Cogenerator Property / A.1.1:
Existence of Full Row Rank KR Representation / A.1.2:
Elimination Property / A.1.3:
State-Kernel Form / A.1.4:
Left/Right-Side Unimodular Transformation / A.1.5:
Proofs of Chapter 5 / A.2:
LPV Series Expansion, Pulse Basis / A.2.1:
LPV Series Expansion, OBFs / A.2.2:
Proofs of Chapter 8 / A.3:
Optimal Partition / A.3.1:
h-Center Relation / A.3.2:
h-Segment Worst-Case Distance / A.3.4:
h-Disc Worst-Case Distance / A.3.6:
Convexity / A.3.7:
Optimal Robust Partition / A.3.8:
Proofs of Chapter 9 / A.4:
Representation of Dynamic Dependence / A.4.1:
References
Index
Introduction / 1:
New Challenges for System Identification / 1.1:
The Birth of LPV Systems / 1.2:
2.

図書

図書
Jan C. Willems ... [et al.], eds
出版情報: Berlin : Springer, c2010  xix, 388 p. ; 24 cm
シリーズ名: Lecture notes in control and information sciences ; 398
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Circuit Theory / Part I:
Old and New Directions of Research in System Theory / Rudolf Kalman
Regular Positive-Real Functions and the Classification of Transformerless Series-Parallel Networks / Jason Zheng Jiang ; Malcolm C. Smith
Ports and Terminals / Jan C. Willems
Control / Part II:
On the Sample Complexity of Probabilistic Analysis and Design Methods / Teodoro Alamo ; Roberto Tempo ; Amalia Luque
A Constant Factor Approximation Algorithm for Event-Based Sampling / Randy Cogill ; Sanjay Lall ; João P. Hespanha
A Unified Approach to Decentralized Cooperative Control for Large-Scale Networked Dynamical Systems / Shinji Hara
Control and Stabilization of Linear Equation Solvers / Uwe Helmke ; Jens Jordan
Nonlinear Output Regulation: A Unified Design Philosophy / Alberto Isidori
An Estimated General Cross Validation Function for Periodic Control Theoretic Smoothing Splines / Maja Karasalo ; Xiaoming Hu ; Clyde F. Martin
Stable H Controller Design for Systems with Time Delays / Hitay Özbay
Dynamic Quantization for Control / Toshiharu Sugie ; Shun-ichi Azuma ; Yuki Minami
Graphs and Networks / Part III:
Convergence of Periodic Gossiping Algorithms / Brian D.O. Anderson ; Changbin Yu ; A. Stephen Morse
Distributed PageRank Computation with Link Failures / Hideaki Ishii
Predicting Synchrony in a Simple Neuronal Network / Sachin S. Talathi ; Pramod P. Khargonekar
Mathematical System Theory / Part IV:
On the Stability and Instability of Padé Approximants / Christopher I. Byrnes ; Anders Lindquist
On the Use of Functional Models in Model Reduction / Paul A. Fuhrmann
Quadratic Performance Verification for Boundary Value Systems / Hisaya Fujioka
Lyapunov Stability Analysis of Higher-Order 2-D Systems / Chiaki Kojima ; Paolo Rapisarda ; Kiyotsugu Takaba
From Lifting to System Transformation / Yoshito Ohta
Contractive Systems with Inputs / Eduardo D. Sontag
Dissipativity and Stability Analysis Using Rational Quadratic Differential Forms
On Behavioral Equivalence of Rational Representations / Harry L. Trentelman
Modeling / Part V:
Modeling and Stability Analysis of Controlled Passive Walking / Kentaro Hirata
An Optimization Approach to Weak Approximation of Lévy-Driven Stochastic Differential Equations / Kenji Kashima ; Reiichiro Kawai
Compound Control: Capturing Multivariate Nature of Biological Control / Hidenori Kimura ; Shingo Shimoda ; Reiko J. Tanaka
Law of Large Numbers, Heavy-Tailed Distributions, and the Recent Financial Crisis / Mathukumalli Vidyasagar
Signal Processing / Part VI:
Markov Models for Coherent Signals: Extrapolation in the Frequency Domain / Roger W. Brockett
Digital Signal Processing and the YY Filter / Bruce Francis
Sparse Blind Source Separation via ℓ1-Norm Optimization / Tryphon T. Georgiou ; Allen Tannenbaum
YY Filter—A Paradigm of Digital Signal Processing / Masaaki Nagahara
System Identification / Part VII:
How to Sample Linear Mechanical Systems / Mattia Bruschetta ; Giorgio Picci ; Alessandro Saccon
A Note on LQ Decomposition in Stochastic Subspace Identification / Tohru Katayama
Modeling Systems Based on Noisy Frequency and Time Domain Measurements / Sanda Lefteriu ; Antonio C. lonita ; Athanasios C. Antoulas
Blind Identification of Polynomial Matrix Fraction with Applications / Kenji Sugimoto
Author Index
Circuit Theory / Part I:
Old and New Directions of Research in System Theory / Rudolf Kalman
Regular Positive-Real Functions and the Classification of Transformerless Series-Parallel Networks / Jason Zheng Jiang ; Malcolm C. Smith
3.

図書

図書
by Yihong Gong and Wei Xu
出版情報: New York : Springer, c2007  xv, 277 p. ; 24 cm
シリーズ名: Multimedia systems and applications series ; v. 30
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目次情報: 続きを見る
Introduction / 1:
Basic Statistical Learning Problems / 1.1:
Categorizations of Machine Learning Techniques / 1.2:
Unsupervised vs. Supervised / 1.2.1:
Generative Models vs. Discriminative Models / 1.2.2:
Models for Simple Data vs. Models for Complex Data / 1.2.3:
Model Identification vs. Model Prediction / 1.2.4:
Multimedia Content Analysis / 1.3:
Unsupervised Learning / Part I:
Dimension Reduction / 2:
Objectives / 2.1:
Singular Value Decomposition / 2.2:
Independent Component Analysis / 2.3:
Preprocessing / 2.3.1:
Why Gaussian is Forbidden / 2.3.2:
Dimension Reduction by Locally Linear Embedding / 2.4:
Case Study / 2.5:
Problems
Data Clustering Techniques / 3:
Spectral Clustering / 3.1:
Problem Formulation and Criterion Functions / 3.2.1:
Solution Computation / 3.2.2:
Example / 3.2.3:
Discussions / 3.2.4:
Data Clustering by Non-Negative Matrix Factorization / 3.3:
Single Linear NMF Model / 3.3.1:
Bilinear NMF Model / 3.3.2:
Spectral vs. NMF / 3.4:
Case Study: Document Clustering Using Spectral and NMF Clustering Techniques / 3.5:
Document Clustering Basics / 3.5.1:
Document Corpora / 3.5.2:
Evaluation Metrics / 3.5.3:
Performance Evaluations and Comparisons / 3.5.4:
Generative Graphical Models / Part II:
Introduction of Graphical Models / 4:
Directed Graphical Model / 4.1:
Undirected Graphical Model / 4.2:
Generative vs. Discriminative / 4.3:
Content of Part II / 4.4:
Markov Chains and Monte Carlo Simulation / 5:
Discrete-Time Markov Chain / 5.1:
Canonical Representation / 5.2:
Definitions and Terminologies / 5.3:
Stationary Distribution / 5.4:
Long Run Behavior and Convergence Rate / 5.5:
Markov Chain Monte Carlo Simulation / 5.6:
Objectives and Applications / 5.6.1:
Rejection Sampling / 5.6.2:
Markov Chain Monte Carlo / 5.6.3:
Rejection Sampling vs. MCMC / 5.6.4:
Markov Random Fields and Gibbs Sampling / 6:
Markov Random Fields / 6.1:
Gibbs Distributions / 6.2:
Gibbs - Markov Equivalence / 6.3:
Gibbs Sampling / 6.4:
Simulated Annealing / 6.5:
Case Study: Video Foreground Object Segmentation by MRF / 6.6:
Objective / 6.6.1:
Related Works / 6.6.2:
Method Outline / 6.6.3:
Overview of Sparse Motion Layer Computation / 6.6.4:
Dense Motion Layer Computation Using MRF / 6.6.5:
Bayesian Inference / 6.6.6:
Solution Computation by Gibbs Sampling / 6.6.7:
Experimental Results / 6.6.8:
Hidden Markov Models / 7:
Markov Chains vs. Hidden Markov Models / 7.1:
Three Basic Problems for HMMs / 7.2:
Solution to Likelihood Computation / 7.3:
Solution to Finding Likeliest State Sequence / 7.4:
Solution to HMM Training / 7.5:
Expectation-Maximization Algorithm and its Variances / 7.6:
Expectation-Maximization Algorithm / 7.6.1:
Baum-Welch Algorithm / 7.6.2:
Case Study: Baseball Highlight Detection Using HMMs / 7.7:
Overview / 7.7.1:
Camera Shot Classification / 7.7.3:
Feature Extraction / 7.7.4:
Highlight Detection / 7.7.5:
Experimental Evaluation / 7.7.6:
Inference and Learning for General Graphical Models / 8:
Sum-product algorithm / 8.1:
Max-product algorithm / 8.3:
Approximate inference / 8.4:
Learning / 8.5:
Discriminative Graphical Models / Part III:
Maximum Entropy Model and Conditional Random Field / 9:
Overview of Maximum Entropy Model / 9.1:
Maximum Entropy Framework / 9.2:
Feature Function / 9.2.1:
Maximum Entropy Model Construction / 9.2.2:
Parameter Computation / 9.2.3:
Comparison to Generative Models / 9.3:
Relation to Conditional Random Field / 9.4:
Feature Selection / 9.5:
Case Study: Baseball Highlight Detection Using Maximum Entropy Model / 9.6:
System Overview / 9.6.1:
Highlight Detection Based on Maximum Entropy Model / 9.6.2:
Multimedia Feature Extraction / 9.6.3:
Multimedia Feature Vector Construction / 9.6.4:
Experiments / 9.6.5:
Max-Margin Classifications / 10:
Support Vector Machines (SVMs) / 10.1:
Loss Function and Risk / 10.1.1:
Structural Risk Minimization / 10.1.2:
Support Vector Machines / 10.1.3:
Theoretical Justification / 10.1.4:
SVM Dual / 10.1.5:
Kernel Trick / 10.1.6:
SVM Training / 10.1.7:
Further Discussions / 10.1.8:
Maximum Margin Markov Networks / 10.2:
Primal and Dual Problems / 10.2.1:
Factorizing Dual Problem / 10.2.2:
General Graphs and Learning Algorithm / 10.2.3:
Max-Margin Networks vs. Other Graphical Models / 10.2.4:
Appendix / A:
References
Index
Introduction / 1:
Basic Statistical Learning Problems / 1.1:
Categorizations of Machine Learning Techniques / 1.2:
4.

図書

図書
[by] Robert L. Shrader
出版情報: New York : McGraw-Hill, [1975]  xvi, 798 p ; 25 cm
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Current, Voltage, and Resistance Direct-Current Circuits Magnetism
Alternating Current
Inductance and Transformers Capacitance
Alternating-Current Circuits
Resonance and LC Filters
Active Devices Power Supplies
Oscillators Digital Fundamentals
Measuring Devices Audio-Frequency
Amplifiers Radio-Frequency
Amplifiers Basic Trans-mitters
Amplitude Modulation and SSB
Amplitude-Modulation Receivers
Frequency Modulation Antennas Two-Way Communications
Microwaves Fiber Optics Broadcast Stations
Television Maritime Radio
Radar Sources of Electricity Operating Fundamentals
Current, Voltage, and Resistance Direct-Current Circuits Magnetism
Alternating Current
Inductance and Transformers Capacitance
5.

図書

図書
William A. Skillman
出版情報: Dedham, Mass. : Artech House, c1983  405 p. ; 29 cm
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6.

図書

図書
prepared by National Oceanic and Atmospheric Administration, United States Department of Commerce and National Aeronautics and Space Administration ; contributing agencies, Agency for International Development ...
出版情報: Washington, D.C. : NASA : NOAA : [National Aeronautics and Space Administration, 1986  xxvii, 126 p. ; 28 cm
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7.

図書

図書
Tsau Young Lin ...[et al.]
出版情報: Berlin : Springer, c2005  xiii, 375 p. ; 25 cm
シリーズ名: Studies in computational intelligence ; v. 6
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8.

図書

図書
B. Bandyopadhyay, S. Janardhanan
出版情報: Berlin : Springer, c2006  xv, 147 p. ; 24 cm
シリーズ名: Lecture notes in control and information sciences ; 323
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9.

図書

図書
福富忠和著
出版情報: 東京 : アスキー, 2000.9  241p ; 19cm
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10.

図書

図書
中村次男, 山本実著
出版情報: 東京 : 山海堂, 2000.8  8, 222p ; 21cm
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