Self-excitation of neurons leads to multiperiodicity of discrete-time neural networks with distributed delays  

作　　者：HUANG ZhenKun WANG XingHua SANNAY Mohamad 

机构地区：[1]School of Sciences, Jimei University, Xiamen 361021, China [2]Department of Mathematics, Zhejiang University, Hangzhou 310027, China [3]Department of Mathematics, Faculty of Science, Universiti Brunei Darussalam, Gadong BE 1410, Brunei Darussalam

出　　处：《Science China(Information Sciences)》2011年第2期305-317,共13页中国科学（信息科学）（英文版）

基　　金：Supported by the National Natural Science Foundation of China (Grant No. 10731060);the Foundation for Young Professors of Jimei University,the Scientific Research Foundation of Jimei University ;the Foundation for Talented Youth with Innovation in Science and Technology of Fujian Province (Grant No. 2009J05009);Training Program Foundation for Distinguished Young Scholars and Research Talents of Fujian Higher Education(Grant No. JA10184)

摘　　要：In this paper, we investigate the interesting multiperiodicity of discrete-time neural networks with excitatory self-connections and distributed delays. Due to self-excitation of neurons, we construct 2N close regions in state space for N-dimensional networks and attain the coexistence of 2N periodic sequence solutions in these close regions. Meanwhile we estimate exponential attrazting domain for each periodic sequence solution and apply our results to discrete-time analogues of periodic or autonomous neural networks. Under self-excitation of neurons, numerical simulations are performed to illustrate the effectiveness of our results.In this paper, we investigate the interesting multiperiodicity of discrete-time neural networks with excitatory self-connections and distributed delays. Due to self-excitation of neurons, we construct 2N close regions in state space for N-dimensional networks and attain the coexistence of 2N periodic sequence solutions in these close regions. Meanwhile we estimate exponential attrazting domain for each periodic sequence solution and apply our results to discrete-time analogues of periodic or autonomous neural networks. Under self-excitation of neurons, numerical simulations are performed to illustrate the effectiveness of our results.

关 键 词：SELF-EXCITATION multiperiodicity discrete-time neural networks distributed delays 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP273.2[自动化与计算机技术—控制科学与工程]