Global exponential almost periodicity of a delayed memristor-based neural networks |
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| Affiliation: | 1. School of Electronic Engineering, University of Electronics Science and Technology of China, Chengdu 611731, China;2. College of Science, China University of Petroleum, Qingdao 266580, China;1. School of Computer Science, Adaptive Systems Research Group University of Hertfordshire, Collage Lane Campus, College Ln, Hatfield, Hertfordshire AL10 9AB, United Kingdom;2. Clermont University, Blaise Pascal University, Pascal Institute, BP 10448, F-63000 Clermont-Ferrand, France;3. CNRS, UMR 6602, Pascal Institute, F-63171 Aubiere, France;4. Istituto di Scienze e Tecnologie della Cognizione - CNR, Via S. Martino della Battaglia, 44 - 00185 Rome, Italy;1. Kanazawa University, Japan;2. Miyazaki University, Japan;3. University of Toyama, Japan;1. Department of Mathematics and Physics, Fujian University of Technology, Fuzhou, Fujian 350118, China;2. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong |
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| Abstract: | In this paper, the existence, uniqueness and stability of almost periodic solution for a class of delayed memristor-based neural networks are studied. By using a new Lyapunov function method, the neural network that has a unique almost periodic solution, which is globally exponentially stable is proved. Moreover, the obtained conclusion on the almost periodic solution is applied to prove the existence and stability of periodic solution (or equilibrium point) for delayed memristor-based neural networks with periodic coefficients (or constant coefficients). The obtained results are helpful to design the global exponential stability of almost periodic oscillatory memristor-based neural networks. Three numerical examples and simulations are also given to show the feasibility of our results. |
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| Keywords: | Memristor-based neural networks Almost periodic solution Global exponential stability |
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