摘要
现基于TL-模Max-TL模糊Hopfield网络(Max-TLFHNN)提出了一种有效的学习算法。对任意给定的模式集合,该学习算法总能找到使该模式集合成为Max-TLFHNN的平衡点集合的所有连接权矩阵中的最大者。任意给定的模式集合都能作为Max-TFHNN网络的平衡点集合且能使Max-TLFHNN对任意输入在一步内就进入稳定状态,同时该网络对训练模式的摄动具有好的鲁棒性。
In this paper,an efficient learning algorithm was proposed for a class of fuzzy Hopfield networks（Max-T FHNNs） based on T-norms.For any given set of patterns,the learning algorithm can find the maximum of all connection weight matrices that can make the set become a set of the equilibrium points of the Max-T FHNN when T is a left-continuous T-norm.This maximal matrix is idempotent matrix in sense of Max-T composition,with which the Max-T FHNN can be convergent to a stable state in one iterative process for any input vector.It is proved theoretically that arbitrary set of patterns can become a set of the equilibrium points of every Max-T FHNN if only the T is left-continuous T-norm.Max-TL FHNN has universally good robustness to perturbation of training pattern.
出处
《计算机科学》
CSCD
北大核心
2010年第12期206-208,共3页
Computer Science
基金
国家自然科学基金项目(No.60632050)
教育部重点科研项目(No.208098)
湖南省教育厅科研基金重点项目(No.07A056)
湖南省教育厅科研基金优秀青年项目(No.10B088)资助
