Identification of Nonlinear Systems Using Neural Networks and Polynomial Models [electronic resource] : A Block-Oriented Approach / by Andrzej Janczak.Material type: TextLanguage: English Series: Lecture Notes in Control and Information Science: 310Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005Description: XIV, 197 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540315964Subject(s): Engineering | Systems theory | Physics | Vibration | Engineering | Control Engineering | Vibration, Dynamical Systems, Control | Systems Theory, Control | ComplexityAdditional physical formats: Printed edition:: No titleOnline resources: Click here to access online
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.