Robustness analysis of neutral BAMNN with time delays

Chunmei Wu, Linli Jiang
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Abstract

This paper is studied with robustness of neutral BAMNN (Bidirectional Associative Memory neural networks) with time delays. We discuss that how much neutral term contraction coefficient and time delay are allowed to ensure the global exponential stable of neutral BAMNN with time delays. By using some transcendental equations, a criterion of global exponential stable is derived for neutral BAMNN with time delays. We proved in theory, if contraction coefficient of neutral terms and time delays are smaller than the results arrived, then the BAMNN also is globally exponentially stable. Finally, an example is provided to show the correctness of our analysis and the effectiveness of the theoretical results.
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具有时滞的中性BAMNN鲁棒性分析
本文研究了具有时滞的中性双向联想记忆神经网络的鲁棒性。讨论了允许多少中性项收缩系数和多少时滞才能保证具有时滞的中性BAMNN全局指数稳定。利用一些超越方程,导出了具有时滞的中立型BAMNN全局指数稳定的判据。从理论上证明,如果中性项和时滞的收缩系数小于得到的结果,则BAMNN也是全局指数稳定的。最后通过实例验证了分析的正确性和理论结果的有效性。
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