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Periodic solutions of high-order Cohen–Grossberg neural networks with distributed delays
Institution:1. Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, T6G 2G1 Canada;2. International College of Manitoba, Winnipeg, Manitoba, R3T 2N2 Canada;1. College of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, China;2. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China;1. College of Mathematics and Econometrics, Hunan University, Changsha, Hunan 410082, PR China;2. Hunan Women’s University, Changsha, Hunan 410002, PR China;1. Institute for Information and System Science, Xi?an Jiaotong University, Xi?an 710049, China;2. College of Mathematics and Statistics, Hubei Normal University, Huangshi 435002, China;3. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China;4. College of Arts and Science, Hubei Normal University, Huangshi 435003, China;1. School of Science, Yanshan University, Qinhuangdao 066001, China;2. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066001, China
Abstract:A class of high-order Cohen–Grossberg neural networks with distributed delays is investigated in this paper. Sufficient conditions to guarantee the uniqueness and global exponential stability of periodic solutions of such networks are established by using suitable Lypunov function and the properties of M-matrix. The results in this paper improve the earlier publications.
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