Fuzzy associative memory network based on parameterized gathering operator

被引：0

作者：

Li, Ying ^{[1
]}

Xu, Wei-Hong ^{[1
,2
]}

Tang, Liang-Rong ^{[1
]}

机构：

[1] College of Computer and Communications Engineering, Changsha University of Science and Technology, Changsha Hunan 410076, China

[2] College of Mathematics and Computer Science, Jishou University, Jishou Hunan 416000, China

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2010年 / 27卷 / 11期

关键词：

Associative storage - Fuzzy inference - Mathematical operators - Fuzzy neural networks - Associative processing - Learning algorithms - Memory architecture;

D O I：

暂无

中图分类号：

学科分类号：

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 Vλ, 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 {Vλ|λ ∈[0,1]}. 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-λ learning 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.

引用

页码：1518 / 1524

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