Identification of Nonlinear Systems Using Neural Networks and Polynomial Models
Springer Berlin (Verlag)
978-3-540-23185-1 (ISBN)
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.
Introduction.- Neural network Wiener models.- Neural network Hammerstein models.- Polynomial Wiener models.- Polynomial Hammerstein models.- Applications.
Erscheint lt. Verlag | 18.11.2004 |
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Reihe/Serie | Lecture Notes in Control and Information Sciences |
Zusatzinfo | XIV, 199 p. |
Verlagsort | Berlin |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 335 g |
Themenwelt | Technik ► Elektrotechnik / Energietechnik |
Schlagworte | Justin • Learning Algorithms • Neural networks • Neuronale Netze • Nichtlineares System • Nichtlineare Systeme • nonlinear system • Nonlinear Systems • Polynomial Models • System Identification |
ISBN-10 | 3-540-23185-4 / 3540231854 |
ISBN-13 | 978-3-540-23185-1 / 9783540231851 |
Zustand | Neuware |
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