Titlebook: Nonlinear System Identification; From Classical Appro Oliver Nelles Textbook 2001 Springer-Verlag Berlin Heidelberg 2001 Automatisierungste

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Textbook 2001ed ideas are illustrated by numerous figures, examples, and real-world applications. Fifteen years ago, nonlinear system identification was a field of several ad-hoc approaches, each applicable only to a very restricted class of systems. With the advent of neural networks, fuzzy models, and modern s

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Textbook 2001nlinear system identification. The reader will be able to apply the discussed models and methods to real problems with the necessary confidence and the awareness of potential difficulties that may arise in practice. This book is self-contained in the sense that it requires merely basic knowledge of

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Unsupervised Learning Techniquesa into another form, which hopefully can be better processed by the subsequent model. In this context, it is important to keep in mind that the desired output is actually available, and there may exist some efficient way to include this knowledge even into the preprocessing phase.

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Introduction to Static Modelspproaches. Chapters 13 and 14 introduce and extend the local linear neuro-fuzzy model architectures and in particular the local linear model tree (LOLIMOT) training algorithm. Finally, the main results of this part are summarized in Chap. 15.

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Introductiontem identification problem. Several modeling paradigms are reviewed in Sect. 1.3. Section 1.4 characterizes the purpose of this book and gives an outline with some reading suggestions. Finally, a few terminological issues are addressed in Sect. 1.5.

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