采用BP算法的模糊自适应控制

在对不熟悉的过程进行模糊控制时,由于对过程的不了解,很难得到合适的控制规则.基于模糊控制器的一种解析结构,提出了将模糊控制器与神经网络相结合的方法.由神经网络对系统进行辨识,并为学习系统提供必要的信息,将控制对象视为神经网络的输出部分,采用BP算法根据神经网络提供的信息对经验规则进行修改,从而改善模糊控制系统动态响应.仿真结果表明该控制器对模型参数变化具有较好的适应能力,能够较快地修改系统的原控制规则,使对象输出较快地跟踪系统的输入.

Fuzzy Adaptive Controller Based on BP Algorithm

When we apply fuzzy control to a unknow process,we can't find good control rule because of we don't know it's characteristics.This paper based on an analytic structure of fuzzy controller put forward a method,which combines fuzzy controller and neural network.Use neural network to make identification of system and provide necessary message for LS.Regard control object as an output part of network.Based on message provided by neural network adopt BP algorithm to modify experience rule.Thus,improve dynamic process of fuzzy control system.Simulation results indicate:this controller has better adaptability for variety of model parameters and can modify original system control rule,makes a output follow system input tracks faster.