模糊Hopfield网络的收敛性与鲁棒性分析  

Convergence and robustness analysis of fuzzy Hopfield neural network

作　　者：刘亮[1] 徐蔚鸿[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院,长沙410114

出　　处：《计算机工程与应用》2014年第23期73-76,共4页Computer Engineering and Applications

基　　金：教育部重点科研项目(No.208098);湖南省科技计划项目(No.2012FJ30052)

摘　　要：收敛性与鲁棒性是模糊神经网络的两个重要性质。对带阈值的Max-T模糊Hopfield神经网络(记为Max-T-C FHNN)的收敛性及在训练模式小幅摄动情况下的鲁棒性进行了分析,从理论上给出了严格的证明。发现了采用最大权值矩阵学习算法时,Max-T-C FHNN具有良好的收敛性,同时当T模及其蕴含算子满足Lipschitz条件时,Max-T-C FHNN对训练模式摄动全局拥有好的鲁棒性,用自联想实验验证了理论的有效性。Convergence and robustness are two important properties of fuzyy neural network. This paper analyses the conver-gence and robustness of Max-T fuzzy Hopfield neural network with threshold（called Max-T-C FHNN）in the condition of perturbations of training patterns, which is proved theoretically. It is discovered that Max-T-C FHNN using maximum weight matrix is of excellent convergence. Max-T-C FHNN holds good robustness globally to perturbations of training patterns in the case that T-norms and its implication operator satisfy the Lipschitz condition. The self-association experiment is given to testify the theoretical results.

关 键 词：阈值 模糊神经网络 Max-T模糊Hopfield神经网络 收敛性 鲁棒性 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]