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

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