Identification of Nonlinear Systems Using Neural Networks and Polynomial Models

A Block-Oriented Approach
2005
ISBN: 978-3-540-23185-1
Verlag: Springer Berlin Heidelberg

Buch, Englisch, Band 310, 199 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 341 g

Reihe: Lecture Notes in Control and Information Sciences

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.

Zielgruppe

Research

Autoren/Hrsg.

Janczak, Andrzej

Fachgebiete

• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Überwachungstechnik
• Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Statik, Dynamik, Kinetik, Kinematik

Inhaltsverzeichnis

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