Abstract: Convergence and robustness are two important properties of fuzyy neural network. This paper analyses the convergence 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.

Key words: threshold, fuzzy neural network, Max-T fuzzy Hopfield neural network, convergence, robustness

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。

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