The BSB neural network in the convex body spanned by the prototype patterns for associative memory
Applied Mathematics and Computation
A new algorithm for implementing BSB-based associative memories
ICC'08 Proceedings of the 12th WSEAS international conference on Circuits
An SVM based method for associative memories
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
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The design problem of generalized brain-state-in-a-box (GBSB) type associative memories is formulated as a constrained optimization program, and “designer” neural networks for solving the program in real time are proposed. The stability of the designer networks is analyzed using Barbalat's lemma. The analyzed and synthesized neural associative memories do not require symmetric weight matrices. Two types of the GBSB-based associative memories are analyzed, one when the network trajectories are constrained to reside in the hypercube [-1, 1]n and the other type when the network trajectories are confined to stay in the hypercube [0, 1]n. Numerical examples and simulations are presented to illustrate the results obtained