摘要
通过在 ANFIS的归一化层与输出层之间加入递归层 ,提出一种新型的动态模糊神经网络(DFNN) ,将模糊推理系统、神经网络和 型控制有机地结合起来。给出了 DFNN的网络结构 ,为基于收缩间距隶属函数和 BP算法提供了参数调整方法。系统实验表明 ,DFNN控制器比 PID+前馈控制具有更好的动、静态响应 ,尤其在前馈信号难以取得的情况下具有更明显的优势。
A novel dynamical neuro fuzzy network (DFNN) is proposed by adding a recurrent layer between the normalized layer and output layer of the forward neuro fuzzy network ANFIS. DFNN combines the advantages of fuzzy system, neural network and type Ⅲ controller. The structure of DFNN and a parameter regulating method which is based on the shrinking span membership functions and BP algorithm are proposed. The experiment results show that DFNN has a better response than the traditional PID+forward controller especially in the situation when the forward signal is difficult to obtain.
出处
《控制与决策》
EI
CSCD
北大核心
2001年第3期347-350,共4页
Control and Decision
基金
国家"九五"重点预研项目! (34 .2 .1)
