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一种模糊逻辑推理神经网络的结构及算法设计
引用本文:韩敏,孙燕楠,许士国.一种模糊逻辑推理神经网络的结构及算法设计[J].控制与决策,2006,21(4):415-420.
作者姓名:韩敏  孙燕楠  许士国
作者单位:1. 大连理工大学,电子与信息工程学院,辽宁,大连,116024
2. 大连理工大学,土木水利学院,辽宁,大连,116024
基金项目:国家自然科学基金重点项目(50139020).
摘    要:建立了一种基于模糊逻辑推理的神经网络.由样本获取的初始规则确定规则层神经元个数,并确立模糊化层与规则层之间的连接.利用黄金分割法确定模糊化层隶属度函数的初始中心和宽度;根据初始规则的结论确定清晰化层的初始权值;针对网络结构提出了改进的BP算法.仿真实例表明,网络结构合理。具有较好的非线性映射能力,改进的BP算法适合于此网络,与另一种模糊神经网络相比较具有较快的训练速度和较好的泛化能力.

关 键 词:模糊逻辑推理  神经网络  规则  黄金分割法
文章编号:1001-0920(2006)04-0415-06
收稿时间:2005-02-02
修稿时间:2005-05-24

Structure and Algorithm Design of a Fuzzy Logic Inference Neural Network
HAN Min,SUN Yan-nan,XU Shi-guo.Structure and Algorithm Design of a Fuzzy Logic Inference Neural Network[J].Control and Decision,2006,21(4):415-420.
Authors:HAN Min  SUN Yan-nan  XU Shi-guo
Affiliation:a. School of Electronic and Information Engineering, b. School of Civil and Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China.
Abstract:A fuzzy neural network is proposed according to the fuzzy logic inference.Initial rules are got from samples and every rule becomes a neuron of the rule layer.How fuzzification layer and rule layer connect with each other is determined by the rules.The initial centers and widths of membership functions of the fuzzification layer are decided based on the golden partition method.And the initial weights of defuzzification layer are determined according to the conclusions of the initial rules.For the structure of the fuzzy neural network an improved back propagation(BP) algorithm is presented.The results of simulation show the nonlinear mapping ability of the fuzzy neural network.Compared with the existed results,the fuzzy neural network has faster training speed and better generalization ability.
Keywords:Fuzzy logic inference  Neural network  Rule  Golden partition method
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