Reducing the tracking error and finding a good arrangement for the control components such as a system's model and a controller have been the main topics of many recent papers. In this relation lack of attention to use internal structure of the Neural Network(NN) is apparently sensible; NNs have been treated as black boxes. The main objective of this article is to use more flexible NN, or parametric NN, to design a better controller. A PNN(parametric NN) can represent both of linear and nonlinear relationships explicitly and simultaneously by setting its parameters appropriately. In many cases we have some information about the system which enable us to build a linear controller for it. But of course this is not enough for treating nonlinear plants. Using PNN we could make a complimentary linearized controller and then, after starting the learning, in an online manner it will be extended to a nonlinear dominant controller.
|出版ステータス||出版済み - 12 1 1995|
|イベント||Proceedings of the 34th SICE Annual Conference - Hokkaido, Jpn|
継続期間: 7 26 1995 → 7 28 1995
|その他||Proceedings of the 34th SICE Annual Conference|
|Period||7/26/95 → 7/28/95|
All Science Journal Classification (ASJC) codes
- コンピュータ サイエンスの応用