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

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Local Linear Neuro-Fuzzy Models: Advanced Aspectsas follows. Section 14.1 discusses the possibility of different input spaces for rule premises and consequents, which is a unique feature of local neuro-fuzzy models. Section 14.2 introduces the use of models that are more complex than local .. An extension of the LOLIMOT algorithm that allows one t

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Linear Dynamic System Identificationmphasize the clear distinction from static systems. An understanding of the basic concepts and the terminology of linear dynamic system identification is required in order to study the identification of . systems, which is the subject of all subsequent chapters. The purpose of this chapter is to int

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978-3-642-08674-8Springer-Verlag Berlin Heidelberg 2001

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Linear, Polynomial, and Look-Up Table Modelsheory and practice. The simplest approach pursued in Sect. 10.1 is to approximate the nonlinear process behavior with a linear model. In the subsequent section, the polynomial approximator is discussed. Finally, in Sect. 10.3 the standard grid-based look-up table with linear interpolation is analyzed.

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Neural Networksres in the brains of humans and animals, which are extremely powerful for such tasks as information processing, learning, and adaptation. Good overviews on the biological background can be found in [326, 328]. The most important characteristics of neural networks are

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Oliver NellesEasy and intuitive understanding.Explanations and terminology from an engineering point-of-view.Only basic mathematics required.Self-contained, no other literature needed.Includes supplementary materi

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