New Book on Neural Networks and Fuzzy Systems for Nonlinear System

From: Oliver Nelles (oliver.nelles@gmx.de)
Date: Fri Mar 30 2001 - 03:39:05 MET DST

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    I would like to announce the publication of my new book on neural
    networks and fuzzy systems for nonlinear system identification:

    Nonlinear System Identification -
    >From Classical Approaches to Neural Networks and Fuzzy Models

    by
    Oliver Nelles, UC Berkeley, now: Siemens Automotive
    oliver.nelles@gmx.de

    Springer, 2001, 785 pp. 422 figs. Hardcover.
    3-540-67369-5
    $ 79.95 or DM 149.00
    http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67369-5

    The book covers the most common and important approaches for the
    identification of nonlinear static and dynamic systems. Additionally, it
    provides the reader with the necessary background on optimization
    techniques making the book self-contained. The emphasis is put on modern
    methods based on neural networks and fuzzy systems without neglecting
    the classical approaches. The entire book is written from an engineering
    point-of-view, focusing on the intuitive understanding of the basic
    relationships. This is supported by many illustrative figures. Advanced
    mathematics is avoided. Thus, the book is suitable for last year
    undergraduate and graduate courses as well as research and development
    engineers in industries.

     1. Introduction 1

    Part I: Optimization
     2. Introduction to Optimization 23
     3. Linear Optimization 35
     4. Nonlinear Local Optimization 79
     5. Nonlinear Global Optimization 113
     6. Unsupervised Learning Techniques 137
     7. Model Complexity Optimization 157
     8. Summary of Part I 203

    Part II: Static Models
     9. Introduction to Static Models 209
    10. Linear, Polynomial, and Lock-Up Table Models 219
    11. Neural Networks 239
    12. Fuzzy and Neuro-Fuzzy Models 299
    13. Local Linear Neuro-Fuzzy Models: Fundamentals 341
    14. Local Linear Neuro-Fuzzy Models: Advanced Aspects 391
    15. Summary of Part II 451

    Part III: Dynamic Models
    16. Linear Dynamic System Identification 457
    17. Nonlinear Dynamic System Identification 547
    18. Classical Polynomial Approaches 587
    19. Dynamic Neuro and Fuzzy Models 587
    20. Dynamic Local Linear Neuro-Fuzzy Models 601
    21. Neural Networks with Internal Dynamics 645

    Part IV: Applications
    22. Applications of Static Models 655
    23. Applications of Dynamic Models 677
    24. Applications of Advanced Methods 709

    Appendix
     A. Vectors and Matrices 735
     B: Statistics 739

    References 757

    Index 779

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