摘要
针对一类不确定非线性多输入多输出复杂系统,根据系统的输入输出数据对,提出一种基于聚类的超闭球模糊神经网络系统.该系统通过改进的模糊聚类方法(FCM)确定模糊规则数,采用高维隶属度函数取代常规的单维隶属度函数,并对隶属度函数中心值和隶属度函数参数采用一步通过算法.所提方法可降低系统的模糊规则数,简化网络计算.此外,当系统的输入输出发生变化时,可实现模糊规则库的在线修改.仿真实例验证了所提方法的有效性.
A hyperball fuzzy neural network algorithm is proposed for modeling of uncertain, high-dimensional and complex nonlinear systems based on clustering. Firstly, an improved fuzzy cluster method（FCM） is given to determine the number of fuzzy rules. The one-dimensional membership functions are replaced by the multi-dimensional membership functions. Then, a one-pass algorithm is presented to calculate the centers and parameters of membership functions. The proposed approach can reduce the number of fuzzy rules and simplify the network calculation. Moreover, the fuzzy rules base can be modified online when the input-output data changes. The simulation results show the effectiveness of the proposed approach.
出处
《控制与决策》
EI
CSCD
北大核心
2011年第12期1803-1807,共5页
Control and Decision
基金
国家自然科学基金面上项目(61074070)
山东省自然科学基金项目(Y2008G07
ZR2009GZ004)
山东省科技攻关项目(2009GG10001029)
关键词
数据
非线性系统
模糊
聚类
多输入多输出
data
nonlinear system： fuzzy
clustering
multi-inputmulti-output
