| Description |
1 online resource (xiv, 197 pages) |
| Series |
Lecture notes in control and information sciences ; 310 |
|
Lecture notes in control and information sciences ; 310.
|
| Contents |
Introduction -- Neural network Wiener models -- Neural network Hammerstein models -- Polynomial Wiener models -- Polynomial Hammerstein models -- Applications |
| Summary |
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 |
| Bibliography |
Includes bibliographical references (pages 187-194) and index |
| Notes |
Print version record |
| In |
OhioLINK electronic book center |
|
SpringerLink |
| Subject |
Nonlinear systems.
|
|
Neural networks (Computer science)
|
|
Block designs.
|
|
Neural Networks, Computer
|
|
Ingénierie.
|
|
Block designs
|
|
Neural networks (Computer science)
|
|
Nonlinear systems
|
| Form |
Electronic book
|
| LC no. |
2004097177 |
| ISBN |
9783540315964 |
|
3540315969 |
|
3540231854 |
|
9783540231851 |
|
