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Guaranteed Storing of Limit Cycles into a Discrete-Time Asynchronous Neural Network
Kenji NOWARA Toshimichi SAITO
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1992/11/25
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks
neural network, associative memory, limit cycles, correlation matrix,
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This article discusses a synthesis procedure of a discrete-time asynchronous neural network whose information is a limit cycle. The synthesis procedure uses a novel connection matrix and can be reduced into a linear epuation. If all elements of desired limit cycles are independent at each transition step, the equation can be solved and all desired limit cycles can be stored. In some experiments, our procedure exhibits much better storing performance than previous ones.