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An Optimization Approach to Design of Generalized BSB Neural Associative Memories

Department of Control and Instrumentation Engineering, Korea University, Chochiwon, Chungnam, 339-800, Korea

Yonmook Park

Department of Information Engineering, Graduate School, Korea University, Chochiwon, Chungnam, 339-800, Korea

This article is concerned with the synthesis of the optimally performing GBSB (generalized brain-state-in-a-box) neural associative memory given a set of desired binary patterns to be stored as asymptotically stable equilibrium points. Based on some known qualitative properties and newly observed fundamental properties of the GBSB model, the synthesis problem is formulated as a constrained optimization problem. Next, we convert this problem into a quasi-convex optimization problem called GEVP (generalized eigenvalue problem). This conversion is particularly useful in practice, because GEVPs can be efficiently solved by recently developed interior point methods. Design examples are given to illustrate the proposed approach and to compare with existing synthesis methods.

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				Z. Zeng and J. Wang Analysis and design of associative memories based on recurrent neural networks with linear saturation activation functions and time-varying delays. Neural Comput., August 1, 2007; 19(8): 2149 - 2182. [Abstract] [Full Text] [PDF]