Dynamic Neural and Fuzzy Models
This short chapter extends the neural networks and neuro-fuzzy models from Part B to the dynamic case. The newly arising issues in that context are discussed, such as the sensitivity with respect to the curse of dimensionality. Due to the tapped delay lin
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Nonlinear System Identification From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes Second Edition
Nonlinear System Identification
Oliver Nelles
Nonlinear System Identification From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes Second Edition
Oliver Nelles University of Siegen Netphen, Germany
ISBN 978-3-030-47438-6 ISBN 978-3-030-47439-3 (eBook) https://doi.org/10.1007/978-3-030-47439-3 1st edition: © Springer-Verlag Berlin Heidelberg 2001 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface to the Second Edition
The second edition of this book contains novel chapters on the following topics: • • • • •
Input selection with local model networks, Chap. 15. Gaussian process models, Chap. 16. Design of experiments applications, Chap. 26. Input selection applications, Chap. 27. Local model network toolbox, Chap. 29.
This book focuses mainly on the key benefits that local model networks offer, compared to alternative model architectures. These features are presented with significant width and depth. Additionally, extensively treated new topics are: • Axis-oblique partitioning strategies, in particular, HILOMOT. • Design of experiment strategies utilizing the special structure of local model networks. • Input selection strategies exploiting the possibility for distinct input spaces in local model networks, namely the input space where the local models live, and the input space where the partitioning takes place. • Nonlinear finite impulse response (NFIR) local model networks that take advantage of the input
