ISBN-10:
0471349119
ISBN-13:
9780471349112
Pub. Date:
02/21/2001
Publisher:
Wiley
Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives / Edition 1

Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives / Edition 1

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Product Details

ISBN-13: 9780471349112
Publisher: Wiley
Publication date: 02/21/2001
Series: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control Series , #21
Pages: 312
Product dimensions: 6.46(w) x 9.49(h) x 0.83(d)

About the Author

IRWIN W. SANDBERG is a chaired professor at the University of Texasat Austin.

JAMES T. LO teaches in the Department of Mathematics andStatistics, University of Maryland.

CRAIG L. FANCOURT is a member of the Adaptive Image and SignalProcessing Group at the Sarnoff Corp. in Princeton, New Jersey.

JOSE C. PRINCIPE is BellSouth Professor in the Electrical andComputer Engineering Department at the University of Florida,Gainesville.

SHIGERU KATAGIRI leads research on speech and hearing at NTTCommunication Science Laboratories, Kyoto, Japan.

SIMON HAYKIN teaches at McMaster University in Hamilton,Ontario, Canada. He has authored or coauthored over a dozen Wileytitles.

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Table of Contents

Preface.

1 Feedforward Neural Networks: An Introduction (SimonHaykin).

1.1 Supervised Learning.

1.2 Unsupervised Learning.

1.3 Temporal Processing Using Feedforward Networks.

1.4 Concluding Remarks.

2 Uniform Approximation and Nonlinear Network Structures(Irwin W. Sandberg).

2.1 Introduction.

2.2 General Structures for Classification.

2.3 Myopic Maps, Neural Network Approximations, and VolterraSeries.

2.4 Separation Conditions and Approximation of Discrete-Time andDiscrete-Space Systems.

2.5 Concluding Comments.

2.6 Appendices.

3 Robust Neural Networks (James T. Lo).

3.1 Introduction.

3.2 Preliminaries.

3.3 General Risk-Sensitive Functionals.

3.4 Approximation of Functions by MLPs.

3.5 Approximation of Functions by RBFs.

3.6 Formulation of Risk-Sensitive Identification of Systems.

3.7 Series-Parallel Identification by Artificial Neural Networks(ANNs).

3.8 Paral lel Identification of ANNs.

3.9 Conclusion.

4 Modeling, Segmentation, and Classification of NonlinearNonstationary Time Series (Craig L. Fancourt and Jose C.Principe).

4.1 Introduction.

4.2 Supervised Sequential Change Detection.

4.3 Unsupervised Sequential Segmentation.

4.4 Memoryless Mixture Models.

4.5 Mixture Models for Processes with Memory.

4.6 Gated Competitive Experts.

4.7 Competitive Temporal Principal Component Analysis.

4.8 Output-Based Gating Algorithms.

4.9 Other Approaches.

4.10 Conclusions.

5 Application of Feedforward Networks to Speech (ShigeruKatagiri).

5.1 Introduction.

5.2 Fundamentals of Speech Signals and ProcessingTechnologies.

5.3 Fundamental Issues of ANN Design.

5.4 Speech Recognition.

5.5 Applications to Other Types of Speech Processing.

5.6 Concluding Remarks.

Index.

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