Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 ight 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.
['Hubert Y. Chan', 'Stanislaw H. Zak']
I am writing a research paper on a idea On neural networks that design neural associative memories
[{"from": "human", "value": "I am writing a research paper on a idea On neural networks that design neural associative memories"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 ight 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."}]