A New Robust Stability Criterion for Dynamical Neural Networks with Mixed Time Delays
| Main Authors: | Guang Zhou, Shouming Zhong |
|---|---|
| Format: | Article |
| Bahasa: | eng |
| Terbitan: |
, 2013
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| Online Access: |
https://zenodo.org/record/1335682 |
| ctrlnum |
1335682 |
|---|---|
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Guang Zhou</creator><creator>Shouming Zhong</creator><date>2013-08-05</date><description>In this paper, we investigate the problem of the existence, uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks, the neutral system has mixed time delays and parameter uncertainties. Under the assumption that the activation functions are globally Lipschitz continuous, we drive a new criterion for the robust stability of a class of neural networks with time delays by utilizing the Lyapunov stability theorems and the Homomorphic mapping theorem. Numerical examples are given to illustrate the effectiveness and the advantage of the proposed main results.</description><identifier>https://zenodo.org/record/1335682</identifier><identifier>10.5281/zenodo.1335682</identifier><identifier>oai:zenodo.org:1335682</identifier><language>eng</language><relation>doi:10.5281/zenodo.1335681</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><source>International Journal of Engineering, Mathematical and Physical Sciences 6.0(2)</source><title>A New Robust Stability Criterion for Dynamical Neural Networks with Mixed Time Delays</title><type>Journal:Article</type><type>Journal:Article</type><recordID>1335682</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal |
| author |
Guang Zhou Shouming Zhong |
| title |
A New Robust Stability Criterion for Dynamical Neural Networks with Mixed Time Delays |
| publishDate |
2013 |
| url |
https://zenodo.org/record/1335682 |
| contents |
In this paper, we investigate the problem of the existence, uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks, the neutral system has mixed time delays and parameter uncertainties. Under the assumption that the activation functions are globally Lipschitz continuous, we drive a new criterion for the robust stability of a class of neural networks with time delays by utilizing the Lyapunov stability theorems and the Homomorphic mapping theorem. Numerical examples are given to illustrate the effectiveness and the advantage of the proposed main results. |
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IOS16997 |
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2022-06-06T05:49:32Z |
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