Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay
We derive a new criterion for checking the global stability of periodic oscillation of bidirectional associative memory (BAM) neural networks with periodic coefficients and distributed delay, and find that the criterion relies on the Lipschitz constants of the signal transmission functions, weights...
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Series: | Journal of Inequalities and Applications |
Online Access: | http://dx.doi.org/10.1155/2009/385298 |
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doaj-4cf3aa4afec74b1cbb9ac6173e222a902020-11-24T21:35:04ZengSpringerOpenJournal of Inequalities and Applications1025-58341029-242X2009-01-01200910.1155/2009/385298Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed DelayHongli LiuZhongjun MaYi WangWe derive a new criterion for checking the global stability of periodic oscillation of bidirectional associative memory (BAM) neural networks with periodic coefficients and distributed delay, and find that the criterion relies on the Lipschitz constants of the signal transmission functions, weights of the neural network, and delay kernels. The proposed model transforms the original interacting network into matrix analysis problem which is easy to check, thereby significantly reducing the computational complexity and making analysis of periodic oscillation for even large-scale networks. http://dx.doi.org/10.1155/2009/385298 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hongli Liu Zhongjun Ma Yi Wang |
spellingShingle |
Hongli Liu Zhongjun Ma Yi Wang Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay Journal of Inequalities and Applications |
author_facet |
Hongli Liu Zhongjun Ma Yi Wang |
author_sort |
Hongli Liu |
title |
Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay |
title_short |
Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay |
title_full |
Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay |
title_fullStr |
Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay |
title_full_unstemmed |
Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay |
title_sort |
global exponential stability of periodic oscillation for nonautonomous bam neural networks with distributed delay |
publisher |
SpringerOpen |
series |
Journal of Inequalities and Applications |
issn |
1025-5834 1029-242X |
publishDate |
2009-01-01 |
description |
We derive a new criterion for checking the global stability of periodic oscillation of bidirectional associative memory (BAM) neural networks with periodic coefficients and distributed delay, and find that the criterion relies on the Lipschitz constants of the signal transmission functions, weights of the neural network, and delay kernels. The proposed model transforms the original interacting network into matrix analysis problem which is easy to check, thereby significantly reducing the computational complexity and making analysis of periodic oscillation for even large-scale networks. |
url |
http://dx.doi.org/10.1155/2009/385298 |
work_keys_str_mv |
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_version_ |
1725946797116882944 |