带参数聚合算子的模糊联想记忆网络

Fuzzy associative memory network based on parameterized gathering operator

中文关键词  模糊神经网络  模糊联想记忆  学习算法  t--范数  模糊关系方程

英文关键词  fuzzy neural network  fuzzy associative memory  learning algorithm  t-norm  fuzzy relational equation

中文摘要

      基于最大运算Max和t--范数T的神经网络模型Max-T FAM是B.Kosko提出的经典模糊联想记忆(FAM)网络的一种重要的广义形式, 其性能有多处不足. 本文利用一种参数化聚合算子_¸, 提出了一种计算简单、易于硬件实现的广义模糊联想记忆(GFAM)网络, 其连接算子从f_¸j¸ 2[0, 1]g 中选取; 从理论上严格证明了GFAM具有一致连续性, 比所有Max-T FAM的映射能力和存储能力强很多; 接着运用模糊关系方程理论提出和分析了GFAM的一种所谓的Max-Min-¸学习算法; 最后用实验对GFAM和Max-T FAM的完整可靠存储能力进行了比较, 并示例了GFAM在图像联想方面的应用.

英文摘要

      The neural network model Max-T FAM with the maximum operation and a t-norm T is an important generalized form of the classical fuzzy associative memory(FAM) network proposed by B.Kosko. This model has several disadvantages in its properties. Using a parameterized aggregating operator _¸ , we present a new generalized fuzzy associative memory(GFAM) network which is simple in computation and easy in implementation by hardware. All conjunctive operators of the interconnections of GFAM are chosen from a cluster f_¸j¸ 2 [0; 1]g. The strict theoretical study reveals that the GFAM is uniformly continuous and has much higher mapping ability and stronger storage capability than all Max-T FAMs. From the theory of fuzzy relational equations, we derive and analyze a so-called Max-Min-λlearning algorithm for GFAM. Experimental comparisons of the storage capability have been made between GFAM and all Max-T FAMs. An application of GFAM to associative images is illustrated.