Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9868476 | Physics Letters A | 2005 | 12 Pages |
Abstract
Discrete-time analogues of integrodifferential equations modeling neural networks with periodic inputs are introduced. The discrete-time analogues are considered to be numerical discretizations of the continuous-time networks and we study their dynamical characteristics. It is shown that the discrete-time analogues preserve the periodicity of the continuous-time networks. By constructing a Lyapunov-type sequence, we obtain easily verifiable sufficient conditions ensuring that every solutions of the discrete-time analogue converge exponentially to the unique periodic solutions.
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Physical Sciences and Engineering
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Physics and Astronomy (General)
Authors
Changyin Sun, Chun-Bo Feng,