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

Por:

Janczak, Andrzej [autor]

Colaborador(es):

SpringerLink (Servicio en línea)

Tipo de material: Texto

TextoSeries Lecture Notes in Control and Information Science ; 310Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005Descripción: xiv, 197 páginas recurso en líneaTipo de contenido:

texto

Tipo de medio:

computadora

Tipo de portador:

recurso en línea

ISBN:

9783540315964

Formatos físicos adicionales: Edición impresa:: Sin títuloRecursos en línea:

Conectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)

Contenidos:

Introduction -- Neural network Wiener models -- Neural network Hammerstein models -- Polynomial Wiener models -- Polynomial Hammerstein models -- Applications.

Resumen: 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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Springer eBooks

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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