Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference (한국전기전자재료학회:학술대회논문집)
- 2004.07b
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- Pages.1234-1238
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- 2004
A Controlled Neural Networks of Nonlinear Modeling with Adaptive Construction in Various Conditions
다변 환경 적응형 비선형 모델링 제어 신경망
- Kim, Jong-Man (Namdo College) ;
- Sin, Dong-Yong (Hanra College)
- Published : 2004.07.05
Abstract
A Controlled neural networks are proposed in order to measure nonlinear environments in adaptive and in realtime. The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between tile output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we have various experiments. And this controller call prove effectively to be control in the environments of various systems.