Identification of Nonlinear Systems Using Neural Networks and Polynomial Models : A Block-Oriented Approach /
By: Janczak, Andrzej [author.].
Contributor(s): SpringerLink (Online service).
Material type:
BookSeries: Lecture Notes in Control and Information Science: 310Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.Description: XIV, 199 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540315964.Subject(s): Engineering | System theory | Statistical physics | Dynamical systems | Vibration | Dynamics | Control engineering | Robotics | Mechatronics | Engineering | Control, Robotics, Mechatronics | Vibration, Dynamical Systems, Control | Systems Theory, Control | Statistical Physics, Dynamical Systems and ComplexityDDC classification: 629.8 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK7627 |
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.
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