Exploiting the separability of linear and nonlinear parameters in radial basis function networks
In intelligent control applications, neural models and controllers are usually designed by performing an off-line training, and then adapting it on-line when placed in the operating environment. It is therefore of crucial importance to obtain a good off-line model by means of a good off-line trainin...
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| Formato: | conferenceObject |
| Idioma: | eng |
| Publicado em: |
2013
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| Texto completo: | http://hdl.handle.net/10400.1/2151 |
| País: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/2151 |
| Resumo: | In intelligent control applications, neural models and controllers are usually designed by performing an off-line training, and then adapting it on-line when placed in the operating environment. It is therefore of crucial importance to obtain a good off-line model by means of a good off-line training algorithm. In this paper a method is presented that fully exploits the linear-nonlinear structure found in Radial Basis Function networks, being additionally applicable to other feed-forward supervised neural networks. The new algorithm is compared with two known hybrid methods. |
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