Identification of Nonlinear Systems Using Neural Networks and Polynomial Models
Janczak, Andrzej.
Identification of Nonlinear Systems Using Neural Networks and Polynomial Models A Block-Oriented Approach / [electronic resource] : by Andrzej Janczak. - XIV, 199 p. online resource. - Lecture Notes in Control and Information Science, 310 0170-8643 ; . - Lecture Notes in Control and Information Science, 310 .
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.
9783540315964
10.1007/b98334 doi
Engineering.
Systems theory.
Physics.
Vibration.
Engineering.
Control Engineering.
Vibration, Dynamical Systems, Control.
Systems Theory, Control.
Complexity.
Identification of Nonlinear Systems Using Neural Networks and Polynomial Models A Block-Oriented Approach / [electronic resource] : by Andrzej Janczak. - XIV, 199 p. online resource. - Lecture Notes in Control and Information Science, 310 0170-8643 ; . - Lecture Notes in Control and Information Science, 310 .
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.
9783540315964
10.1007/b98334 doi
Engineering.
Systems theory.
Physics.
Vibration.
Engineering.
Control Engineering.
Vibration, Dynamical Systems, Control.
Systems Theory, Control.
Complexity.