title: Global exponential almost periodicity of a delayed memristor-based neural networks.
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.
authors.affiliation: School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China. Electronic address: chenjiejie118@gmail.com.
authors.forename: Jiejie
authors.initials: J
authors.lastname: Chen
authors.affiliation: School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China. Electronic address: hustzeng527@gmail.com.
authors.forename: Zhigang
authors.initials: Z
authors.lastname: Zeng
authors.affiliation: School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China. Electronic address: jiangping20140209@gmail.com.
authors.forename: Ping
authors.initials: P
authors.lastname: Jiang
mesh_terms: D016571:Neural Networks, Computer; D010507:Periodicity
keywords: Almost periodic solution; Global exponential stability; Memristor-based neural networks