On neural networks that design neural associative memories

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]/sup n/ and the other type when the network trajectories are confined to stay in the hypercube [0, 1]/sup n/. Numerical examples and simulations are presented to illustrate the results obtained.