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Identification of Nonlinear Systems Using Neural Networks and Polynomial Models [electronic resource] :A Block-Oriented Approach / by Andrzej Janczak.

by Janczak, Andrzej [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Control and Information Science: 310Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.Description: XIV, 197 p. online resource.ISBN: 9783540315964.Subject(s): Engineering | Systems theory | Physics | Vibration | Engineering | Control Engineering | Vibration, Dynamical Systems, Control | Systems Theory, Control | ComplexityOnline resources: Click here to access online
Contents:
Introduction -- Neural network Wiener models -- Neural network Hammerstein models -- Polynomial Wiener models -- Polynomial Hammerstein models -- Applications.
In: Springer eBooksSummary: This monograph systematically presents the existing identification methods of nonlinear systems using the block-oriented approach It surveys various known approaches to the identification of Wiener and Hammerstein systems which are applicable to both neural network and polynomial models. The book gives a comparative study of their gradient approximation accuracy, computational complexity, and convergence rates and furthermore presents some new and original methods concerning the model parameter adjusting with gradient-based techniques. "Identification of Nonlinear Systems Using Neural Networks and Polynomal Models" is useful for researchers, engineers and graduate students in nonlinear systems and neural network theory.
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Introduction -- Neural network Wiener models -- Neural network Hammerstein models -- Polynomial Wiener models -- Polynomial Hammerstein models -- Applications.

This monograph systematically presents the existing identification methods of nonlinear systems using the block-oriented approach It surveys various known approaches to the identification of Wiener and Hammerstein systems which are applicable to both neural network and polynomial models. The book gives a comparative study of their gradient approximation accuracy, computational complexity, and convergence rates and furthermore presents some new and original methods concerning the model parameter adjusting with gradient-based techniques. "Identification of Nonlinear Systems Using Neural Networks and Polynomal Models" is useful for researchers, engineers and graduate students in nonlinear systems and neural network theory.

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