摘要
提出了一种递归模糊神经网络(RFNN),通过加入向量调节层,提高了网络对输入信息的处理能力。基于所设计的递归模糊神经网络,建立非线性系统的离散数学多步模糊预测模型,根据这一模型对系统的输出进行预测,然后利用预测控制算法得到相应的预测控制规律。仿真结果表明该方法具有较高的控制精度以及一定的抗干扰能力。
A kind of recurrent fuzzy neural network（RFNN） is constructed,in which,the a bility of the input information handling is enhanced by adding the vector adjustment layer.Based on the designed recursion fuzzy neural network,nonlinear system＇s discrete mathematics multi-step fuzzy forecast model is established.This model is used to forecast the system＇s output,and the corresponding forecast control law is obtained by the existing predictive control algorithm.The simulation result indicates that this method has the high control precision as well as moderate certain anti-interference ability.
出处
《太原理工大学学报》
CAS
北大核心
2012年第1期11-15,共5页
Journal of Taiyuan University of Technology
基金
山西省自然科学基金资助项目(2011011011-1)
关键词
归模糊神经网络
向量调节
广义预测控制
非线性
recurrent fuzzy neural network
vector adjustment
generalized predictive control
nonlinear
