A GA-NN model recognition method for time-varying large delay system

被引：0
|
作者：
Pan Feng [1 ]
Han Rucheng [1 ]
Zhi Zeying [1 ]
机构：
[1] Taiyuan Univ Sci & Technol, Elect Coll, Taiyuan 030024, Peoples R China
来源：
CHINESE JOURNAL OF ELECTRONICS | 2006年 / 15卷 / 4A期
关键词：
genetic algorithm (GA); radial basis function neural network (RBFNN); Smith predictor; large delay system; model recognition;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, A serial-parallel Genetic algorithm-Radial basis function neural network (GA-RBFNN) structure is presented to recognize the mathematical model of time -varying large delay system. In this method, GA is used to identify the time-varying delay characteristics of process, so the delay can be separated from the mathematical model, the recognition result of GA is one input of RBFNN, RBFNN is used to identify other time-varying parameters of process, the training of this RBFNN structure is faster then single neural network structure. After the mathematics model of process is recognized, Smith Predictor based on the recognition model is applied to compensate time-varying delay. The simulation results of a boiler backwater control system indicate the effectiveness of this method.
引用
收藏
页码：887 / 890
页数：4
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