Using RBF Neural Network for OptimumControl of a Cold Storage

作     者：Shi Guodong Wang Qihong Xu Yan Xue Guoxin 

作者机构：Jiangsu Institution of Petrochemical TechnologyChangzhou 213016 P. R. China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2000年第11卷第4期

页      面：30-36页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 081104[工学-模式识别与智能系统] 08[工学] 0802[工学-机械工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Algorithms Cold storage Functions Interpolation Neural networks Online systems Predictive control systems 

摘      要：In recent years, advanced control technologies have been used for the optimum control of a cold storage. But there are still a lot of shortcomings. One of the main problems is that the traditional methods can t realize the on-line predictive optimum control of a refrigerating system with simple and valid algorithms. An RBF neural network has a strong ability in nonlinear mapping, a good interpolating value performance, and a higher training speed. Thus a two-stage RBF neural network is proposed in this paper. Combining the measured values with the predicted values, the two-stage RBF neural network is used for the on-line predictive optimum control of the cold storage temperature. The application results of the new methods show a great success.