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在线知识获得智能模糊控制器研究
引用本文:王淑青,杨桦,刘辉.在线知识获得智能模糊控制器研究[J].计算机与现代化,2006(3):17-19.
作者姓名:王淑青  杨桦  刘辉
作者单位:湖北工业大学电气与电子工程学院,湖北,武汉,430068
摘    要:对一些复杂的系统。传统PID或模糊控制很难得到满意控制效果,本文提出采用基于RBF神经网络和遗传算法的自适应模糊控制器来进行控制。由遗传算法在线优化模糊控制器的比例因子、模糊推理规则和隶属函数。并由RBF网络辨识被控对象的动态特性,以评价模糊控制器控制性能。仿真实验表明。优化后的Fuzzy控制器具有较强的学习和自适应控制能力,控制效果优于没有寻优的Fuzzy控制。

关 键 词:模糊控制  遗传算法  RBF神经网络  水轮发电机组
文章编号:1006-2475(2006)03-0017-03
收稿时间:2005-05-15
修稿时间:2005年5月15日

Research on Online Knowledge Acquiring Intelligent Fuzzy Controller
WANG Shu-qing,YANG Hua,LIU Hui.Research on Online Knowledge Acquiring Intelligent Fuzzy Controller[J].Computer and Modernization,2006(3):17-19.
Authors:WANG Shu-qing  YANG Hua  LIU Hui
Affiliation:School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068,China
Abstract:It is difficult to acquire satisfying control effect for conventional controller,such as PID and fuzzy control,to control complex system.A new self-tuning fuzzy governor based on RBF neural networks and genetic algorithms(GA) is designed.The parameters,rules and member functions of fuzzy controller are optimized based on GA in operating.Dynamic identification model of control system is designed based on the RBF neural networks to appraise the controlling performance of fuzzy controller.Simulation results show that the designed controller has strong learning and adapting capability and its control effect is better than traditional fuzzy controller.
Keywords:fuzzy control  genetic algorithm  RBF neural networks  hydraulic generating unit
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