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

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Linear Optimizationation problem arises. Also, a linear optimization problem can be artificially generated if the error is a nonlinear function .(•) of the parameters but the loss functions is chosen as a sum of those inverted nonlinearities .(•). of the errors. Note, however, that this loss function may not be suitab

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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 analyze

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Fuzzy and Neuro-Fuzzy ModelsThese approaches are commonly referred to as neuro-fuzzy networks since they exploit some links between fuzzy systems and neural networks. Within this chapter only one architecture of neuro-fuzzy networks is considered, the so-called singleton approach. Neuro-fuzzy networks based on local linear mod

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