Identification of Nonlinear Systems Using Neural Networks and Polynomial Models A Block-Oriented Approach /

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 gi...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Janczak, Andrzej (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005.
Edición:1st ed. 2005.
Colección:Lecture Notes in Control and Information Sciences, 310
Temas:
Control engineering.
Robotics.
Automation.
Multibody systems.
Vibration.
Mechanics, Applied.
System theory.
Control theory.
Mathematical physics.
Control, Robotics, Automation.
Multibody Systems and Mechanical Vibrations.
Systems Theory, Control .
Complex Systems.
Theoretical, Mathematical and Computational Physics.
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Identification of Nonlinear Systems Using Neural Networks and Polynomial Models  |h [electronic resource] :  |b A Block-Oriented Approach /  |c by Andrzej Janczak. 
250 |a 1st ed. 2005. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2005. 
300 |a XIV, 199 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a Lecture Notes in Control and Information Sciences,  |x 1610-7411 ;  |v 310 
505 0 |a Introduction -- Neural network Wiener models -- Neural network Hammerstein models -- Polynomial Wiener models -- Polynomial Hammerstein models -- Applications. 
520 |a 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. 
650 0 |a Control engineering. 
650 0 |a Robotics. 
650 0 |a Automation. 
650 0 |a Multibody systems. 
650 0 |a Vibration. 
650 0 |a Mechanics, Applied. 
650 0 |a System theory. 
650 0 |a Control theory. 
650 0 |a Mathematical physics. 
650 1 4 |a Control, Robotics, Automation. 
650 2 4 |a Multibody Systems and Mechanical Vibrations. 
650 2 4 |a Systems Theory, Control . 
650 2 4 |a Complex Systems. 
650 2 4 |a Theoretical, Mathematical and Computational Physics. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783540804123 
776 0 8 |i Printed edition:  |z 9783540231851 
830 0 |a Lecture Notes in Control and Information Sciences,  |x 1610-7411 ;  |v 310 
856 4 0 |u https://doi.uam.elogim.com/10.1007/b98334  |z Texto Completo 
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912 |a ZDB-2-SXE 
912 |a ZDB-2-LNI 
950 |a Engineering (SpringerNature-11647) 
950 |a Engineering (R0) (SpringerNature-43712) 

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