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一种算法改进的模糊神经网络及其性能分析
引用本文:邵俊倩.一种算法改进的模糊神经网络及其性能分析[J].价值工程,2011,30(30):269-271.
作者姓名:邵俊倩
作者单位:绥化学院数学与信息科学学院,绥化,152061
基金项目:绥化学院杰出青年基金资助项目《基于神经网络的综合方法的研究》,项目编号:SJ11006
摘    要:将T-S模糊模型与前馈神经网络相融合构造了一种新型的模糊神经网络,该模型采用基于梯度下降法和算法相结合的混合学习方法,其中梯度下降法用来训练高斯型隶属度函数的非线性参数,而算法用来训练线性参数,即权值。从理论上,证明了该模型对非线性函数的万能逼近能力。仿真实验表明,该模糊神经网络用于非线性动态系统辨识的有效性。

关 键 词:T-S模糊模型  神经网络  系统辨识

An Fuzzy Neural Network of Improved Algorithm and Its Performance Analysis
Shao Junqian.An Fuzzy Neural Network of Improved Algorithm and Its Performance Analysis[J].Value Engineering,2011,30(30):269-271.
Authors:Shao Junqian
Institution:Shao Junqian(College of Mathematics and Informational Science,Suihua University,Suihua 152061,China)
Abstract:Based on the combination of T-S fuzzy model and the feedforward neural networks,this paper researches a new model,called fuzzy neural network,and research a mixed learning algorithm based on the combination of gradient descent algorithm and algorithm.Here,the nonlinear parameters are learned by gradient descent algorithm and linear parameters,viz.,weights are learned by algorithm.The approximation capabilities of the model to nonlinear function are theoretically proved.Finally,the effectiveness of the propo...
Keywords:T-S Fuzzy Model  neural network  system identification  
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