Nonlinear Modeling: Advanced Black-Box Techniques

264 Pages·1998·7.892 MB·English

by  Jonas Sjöberg, Lester S. H. Ngia (auth.), Johan A. K. Suykens, Joos Vandewalle (eds.)

NONLINEAR MODELING
ADVANCED BLACK-BOX TECHNIQUES
NONLINEAR MODELING
ADV ANCED BLACK-BOX TECHNIQUES
Edited by
Johan A. K. Suykens
and
Joos Vandewalle
Katholieke Universiteit Leuven,
Belgium
SPRINGER-SCIENCE+BUSINESS MEDIA, B.V.
A C.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN 978-1-4613-7611-8 ISBN 978-1-4615-5703-6 (eBook)
DOI 10.1007/978-1-4615-5703-6
Printed on acid-free paper
AU Rights Reserved
© 1998 Springer Science+Business Media Dordrecht
Originally published by Kluwer Academic Publishers, Boston in 1998
Softcover reprint of the hardcover 1s t edition 1998
No part of the material protected by this copyright notice may be reproduced or
utilized in any form or by any means, electronic or mechanical,
including photocopying, recording or by any information storage and
retrieval system, without written permission from the copyright owner.
Contents
Preface xi
Contributing Authors xv
1
Neural Nets and Related Model Structures for Nonlinear System Identification 1
Jonas Sjoberg and Lester S.H. Ngia
1.1 Introduction 2
1.2 Black-box Identification Approach 3
1.3 Fitting the Parameters 4
1.4 The Nonlinear Mapping 5
1.5 Some Standard Theory on Model Properties 7
1.5.1 Approximation Properties 7
1.5.2 Bias-Variance 8
1.6 Bias-Variance Trade-off 9
1.6.1 Regularization 10
1.6.2 Implicit Regularization. Stopped Iterations. and Overtraining 11
1.6.3 Pruning and Shrinking 12
1. 7 Discussion 12
1.8 Dynamic Systems 13
1.9 A stepwise Approach to Nonlinear Black-box Identification 15
1.10 Examples 17
1.10.1 Nonlinear Modeling of Hybrid Echo in a Telephone Network 17
1.10.2 Example of Physical Modeling: Aerodynamic Identification 22
1.11 Conclusions 25
References 26
2
Enhanced Multi-Stream Kalman Filter Training for Recurrent Networks 29
Lee A. Feldkamp, Danil V. Prokhorov, Charles F. Eagen, and F'umin Yuan
2.1 Introduction 29
2.2 Network Architecture and Execution 31
2.3 Gradient Calculation 33
2.3.1 Traditional BPTT 34
v
vi NONLINEAR MODELING
2.3.2 Aggregate BPTT 36
2.4 EKF Multi-Stream Training 37
2.4.1 The Kalman Recursion 37
2.4.2 Multi-Stream Training 38
2.5 A Modeling Example 40
2.6 Alternating training of weights and initial states 42
2.7 Toward more coordinated training of weights and states-A Motivating Ex-
ample 44
2.8 The Mackey-Glass Series 46
2.9 Use of a structured evolver 49
2.10 Summary 51
References 52
3
The Support Vector Method of Function Estimation 55
Vladimir Vapnik
3.1 Introduction 55
3.2 The Optimal Hyperplane 56
3.2.1 The Optimal Hyperplane for Separable Data 56
3.2.2 The Optimal Hyperplane for Nonseparable Data 62
3.2.3 Statistical Properties of the Optimal Hyperplane 64
3.3 The Support Vector Machine for Pattern Recognition 66
3.3.1 The Idea of Support Vector Machines 66
3.3.2 Generalization in High-Dimensional Space 67
3.3.3 Hilbert-Schmidt theory and Mercer's theorem 67
3.3.4 Constructing SV Machines 68
3.3.5 Selection of a SV Machine Using Bounds 69
3.4 Examples of SV Machines for Pattern Recognition 70
3.4.1 Polynomial Support Vector Machines 71
3.4.2 Radial Basis Function SV Machines 71
3.4.3 Two-Layer Neural SV Machines 72
3.5 The SVM for regression estimation 73
3.5.1 €-Insensitive Loss-Functions 73
3.5.2 Minimizing the Risk with €-insensitive Loss-function 74
3.5.3 SV Machines for Regression Estimation 78
3.6 Kernels for Estimating Real-Valued Functions 78
3.6.1 Kernels Generating Splines 79
3.6.2 Kernels Generating Splines with an Infinite Number of Knots 80
3.6.3 Kernels Generating Fourier Expansions 81
3.7 Solving Operator Equations 82
3.8 Conclusion 84
References 84
4
Parametric Density Estimation for the Classification of Acoustic Feature Vec- 87
tors in Speech Recognition
Sankar Basu and Charles A. Micchelli
4.1 Introduction 87
4.2 The Probability Densities 89
4.3 Maximum Likelihood Estimation 96
Contents vii
4.4 Mixture Models 105
4.5 Numerical experiment 112
4.6 Conclusion 114
References 114
5
Wavelet Based Modeling of Nonlinear Systems 119
Yi Yu, Wayne Lawtont Seng Luan Leet, Shaohua Tant ,Joos Vandewalle#
5.1 Introduction 120
5.2 Wavelet Interpolation Method 123
5.2.1 Wavelet-Based Norms 123
5.2.2 Interpolation Method 126
5.2.3 Interpolation Algorithm 127
5.3 Wavelet Interpolation in Sobolev Space 129
5.3.1 Wavelet-based Sobolev Norms 129
5.3.2 Interpolation Method 130
5.3.3 Spatial Adaptivity 133
5.3.4 Multi-scale Interpolation 134
5.4 Numerical Examples 136
5.4.1 Nonlinear System Modeling from Random Observations 138
5.4.2 Dynamical System Modeling from Time Series 140
5.5 Conclusions 141
Appendix: Constrained Minimal Norm Interpolation 143
References 146
6
Nonlinear Identification based on Fuzzy Models 149
Vincent Wertz* and Stephen Yurkovich**
6.1 Introduction 149
6.1.1 Motivations 149
6.1.2 Chapter outline 151
6.2 Functional fuzzy models 151
6.2.1 Fuzzy models 151
6.2.2 Takagi-Sugeno fuzzy models 154
6.2.3 Relation with LT V-LPV models and control issues 155
6.2.4 Structure, nonlinear and linear parameters 155
6.3 Fuzzy Modelling algorithms without structure determination 157
6.3.1 Fixed shape for the membership functions 157
6.3.2 Estimation of nonlinear parameters - ANFIS 158
6.4 Structure determination 160
6.4.1 Clustering methods 160
6.4.2 Subtractive clustering 163
6.5 A worked example 164
6.5.1 The glass furnace process and data 164
6.5.2 Linear identification results 166
6.5.3 TS Fuzzy models 167
6.6 Discussion 172
References 174
viii NONLINEAR MODELING
7
Statistical Learning in Control and Matrix Theory 177
M. Vidyasagar
7.1 Introduction 178
7.2 Paradigm of Controller Synthesis Problem 181
7.3 Various Types of "Near" Minima 184
7.4 A General Approach to Randomized Algorithms 187
7.4.1 The UCEM Property 187
7.4.2 An Approach to Finding Approximate Near Minima with High Con-
~~~ ~8
7.4.3 A Universal Algorithm for Finding Probable Near Minima 190
7.4.4 An Algorithm for Finding Probably Approximate Near Minima 191
7.5 Some Sufficient Conditions for the UCEM Property 194
7.5.1 Definitions of the VC-dimension and P-Dimension 194
7.5.2 Finiteness of the VC- and P-Dimensions Implies the UCEM Property 195
7.5.3 Upper Bounds for the VC-Dimension 196
7.6 Robust Stabilization 198
7.7 Weighted Hoo-Norm Minimization 201
7.8 Randomized Algorithms for Matrix Problems 201
7.9 Sample Complexity Issues 204
7.10 Conclusions 205
References 206
8
Nonlinear Time-Series Analysis 209
Ulrich Parlitz
8.1 Introduction 209
8.2 State space reconstruction 210
8.3 Influence of the reconstruction parameters 213
8.3.1 Choice of the delay time 213
8.3.2 Choice of the embedding dimension 215
8.3.3 Estimating reconstruction parameters 215
8.4 Noise reduction 216
8.4.1 Linear Filters 216
8.4.2 Nonlinear signal separation 217
8.5 Detecting nonlinearities 217
8.6 Modelling and prediction 218
8.7 Fractal dimensions 219
8.7.1 Correlation dimension 219
8.7.2 Information dimension 219
8.8 Lyapunov exponents 221
8.8.1 Jacobian-based methods 222
8.8.2 Direct methods 222
8.9 Synchronization of chaotic dynamics 224
8.9.1 Estimating model parameters 224
8.9.2 Generalized synchronization 224
8.10 Spatio-temporal time series 225
8.10.1 Linear decomposition into spatial modes 226
8.10.2 Local models 227
8.10.3 Numerical Example 229
Contents ix
References 232
9
The K.U.Leuven Time Series Prediction Competition 241
Johan A.K. Suykens and Joos Vandewalle
9.1 Introduction 241
9.2 Origin of the data 242
9.3 Results of the competition 246
9.4 Conclusions 251
References 251
Index 254
Preface
This book aims at presenting advanced black-box techniques for nonlinear mod
eling. The rapid growth of the fields of neural networks, fuzzy systems and
wavelets is offering a large variety of new methods for modeling static and dy
namical nonlinear systems. Therefore, it is important to understand what are
the opportunities, limitations and pitfalls of the several approaches, in order
to obtain reliable designs towards real-life applications. The topic of nonlinear
modeling has been studied from different points of view including statistics,
identification and control theory, approximation theory, signal processing, non
linear dynamics, information theory, physics and optimization theory among
others. The present book has been composed at the occasion of the interna
tional workshop on Advanced Black-Box Techniques for Nonlinear Modeling:
Theory and Applications, held at the K.U. Leuven Belgium July 8-10 1998,
which served as an interdisciplinary forum for research specialists working in
this area. This book surveys the major alternative methods and hence ad
dresses both novice as well as experienced researchers.
In Chapter 1 J. Sjoberg and L. Ngia discuss neural nets and related model
structures for nonlinear system identification. Parametrizations by multilayer
perceptrons, radial basis function networks and hinging hyperplanes are ex
plained. Rather than motivating these models by their universal approxima
tion ability, it has been studied from a bias-variance perspective. The role of
regularization and early stopping is explained. The ideas are illustrated on
examples of nonlinear modeling of hybrid echo in a telephone network and
physical modeling in aerodynamic identification.
In Chapter 2 L. Feldkamp, D. Prokhorov, C. Eagen and F. Yuan present a
framework for the training of time-lagged recurrent neural networks. They ex
plain various forms of backpropagation through time and multi-stream Kalman
filter training for training such recurrent neural networks. They discuss the ini
tial state problem from the standpoint of making time-series predictions.
In Chapter 3 V. Vapnik describes the Support Vector technique for function
estimation problems such as pattern recognition, regression estimation and solv-
Xl
xu NONLINEAR MODELING
ing linear operator equations. While classical neural network techniques suffer
from the problem of many local minima, in Support Vector Machines one has
to solve a quadratic programming problem. Moreover, it is shown that for the
Support Vector method both the quality of solution and the complexity of the
solution does not depend directly on the dimensionality of the input space.
In Chapter 4 S. Basu and C. Micchelli study Expectation Maximization
(EM) type algorithms for the estimation of parameters for a mixture model
of nongaussian densities in a maximum likelihood framework. It is related to
the problem of automatic machine recognition of speech for the classification
of acoustic feature vectors, which is known to be a high dimensional problem.
In Chapter 5 the nonlinear system modeling problem is formulated by Y. Yu,
W. Lawton, S. Tan, S. Lee and J. Vandewalle as a scattered data interpolation
problem. A method is developed that computes interpolants that minimize
a wavelet-based norm subject to interpolatory constraints. Kernels are con
sidered that, in contrast to radial basis function kernels, are not translation
invariant.
In Chapter 6 V. Wertz and S. Yurkovich discuss nonlinear identification
based on fuzzy models. Different choices for the model structure are explained
and illustrated on the identification of industrial glass furnace data.
In Chapter 7 M. Vidyasagar explains the role of statistical learning in sev
eral problems of control and matrix theory that are NP-hard. Randomized
algorithms are used for solving problems in an approximate instead of an exact
sense. Using Vapnik-Chervonenkis (VC) dimension theory it is shown that the
uniform convergence of empirical means property holds in several problems of
robust control.
In Chapter 8 U. Parlitz presents an overview of state space based methods
for nonlinear time-series analysis. Questions of state space reconstruction, pre
diction, filtering, noise reduction and detecting nonlinearities in time series are
addressed. Examples of spatio-temporal systems and chaos synchronization are
given.
Finally, in Chapter 9 we present the results for the time-series prediction
competition, which has been held in the framework of the international work
shop, related to this book. The data are related to a 5-scroll attract or , gener
ated from a generalized Chua's circuit. The winning contribution obtains an
accurate prediction over a time horizon of 300 points using a nearest trajectory
method, which incorporates local modeling and cross-validation techniques.
Johan Suykens
Joos Vandewalle
K.U. Leuven, Belgium