An architecture-adaptive neural network online control system

被引：0
|
作者：
Xun Liang
Rong-Chang Chen
Jian Yang
机构：
[1] Peking University,Institute of Computer Science and Technology
[2] Stanford University,Department of Economics and Operations Research
[3] National Taichung Institute of Technology,Department of Logistics Engineering and Management
[4] Peking University,Institute of Computer Science and Technology
来源：
Neural Computing and Applications | 2008年 / 17卷
关键词：
Hidden neurons; Pruning; Orthogonal projection; Control system;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
An architecture-adaptive intelligent self-tuning control system is presented. The system is composed of the supervisor module, the model refinement module, the process plant and the database. In the supervisor module, the user prescribes the desired curve for the plant dynamic process. The model refinement module is in parallel with the process plant, and consists of the self-tuning process model, which contains an architecture-adaptive neural network. The model refinement module could learn intelligently the real process plant by the prompt adjustments based on the difference of the outputs of the two modules, and its learned model is also refined gradually. This diagram is especially versatile in the complex nonlinear and time-variant systems in practice.
引用
收藏
页码：413 / 423
页数：10
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