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Periodic Solution to BAM-type Cohen-Grossberg Neural Network with Time-varying Delays

Periodic Solution to BAM-type Cohen-Grossberg Neural Network with Time-varying Delays
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摘要 By using the continuation theorem of Mawhin＇s coincidence degree theory and the Liapunov func tional method, some sufficient conditions are obtained to ensure the existence, uniqueness and the global exponential stability of the periodic solution to the BAM-type Cohen-Grossberg neural networks involving timevarying delays. By using the continuation theorem of Mawhin＇s coincidence degree theory and the Liapunov func tional method, some sufficient conditions are obtained to ensure the existence, uniqueness and the global exponential stability of the periodic solution to the BAM-type Cohen-Grossberg neural networks involving timevarying delays.
作者 An-ping Chen Qun-hua Gu
机构地区 Department of Mathematics School of Mathematics and Computational Science
出处 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2011年第3期427-442,共16页 应用数学学报（英文版）
基金 Supported by the National Natural Science Foundation of China (No. 10971173) the Scientific Research Foundation of Hunan Provincial Educational Department (No. 05A057) supported by the Aid Program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province the Construct Program of the Key Discipline in Hunan Province
关键词 periodic solution time-varying delays global exponential stability BAM-type Cohen-Grossberg neural network coincidence degree theory periodic solution, time-varying delays, global exponential stability, BAM-type Cohen-Grossberg neural network, coincidence degree theory
分类号 TP18 [自动化与计算机技术—控制理论与控制工程] TP13 [自动化与计算机技术—控制理论与控制工程]
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参考文献24

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Acta Mathematicae Applicatae Sinica
2011年 第3期

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