Training multilayer perceptrons for control systems applications - a comparison of different approaches
| Data(s) |
13/02/2013
13/02/2013
1996
28/01/2013
|
|---|---|
| Identificador |
Ruano, A. E. Training multilayer perceptrons for control systems applications - a comparison of different approaches, Trabalho apresentado em Int. Conf. on Engineering Applications of Neural Networks (EANN’96), In Int. Conf. on Engineering Applications of Neural Networks (EANN’96), London, 1996. AUT: ARU00698; |
| Idioma(s) |
eng |
| Direitos |
restrictedAccess |
| Tipo |
conferenceObject |
| Resumo |
Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large field of applications. In control and signal processing applications, MLPs are mainly used as nonlinear mapping approximators. The most common training algorithm used with MLPs is the error back-propagation (BP) alg. (1). |
