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Input-to-state stability of impulsive inertial memristive neural networks with time-varying delayed
Authors:Wei Zhang  Jiangtao Qi  Xing He
Institution:1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Department of Electronics and Information Engineering, Southwest University, Chongqing 400715, China;2. Key laboratory of Machine Perception and Children’s Intelligence Development, Chongqing University of Education, Chongqing 400067, PR China;3. School of Information Science and Electrical Engineering, Shan Dong Jiaotong University, Jinan 250357, China
Abstract:The property of input-to-state stability (ISS) of inertial memristor-based neural networks with impulsive effects is studied. Firstly, according to the characteristics of memristor and inertial neural networks, the inertial memristor-based neural networks are built. Secondly, based on the impulsive control theory, the average impulsive interval approach, Halanay differential inequality, Lyapunov method and comparison property, some sufficient conditions ensuring ISS of the inertial memristor-based neural networks under impulsive controller are derived. In this paper, we consider two types of impulse, stabilizing impulses and destabilizing impulses. When the inertial memristor-based neural networks are originally not ISS, by choosing a suitable lower bound of the average impulsive interval, the stabilizing impulses can be used to stabilize the inertial memristor-based neural networks. On the contrary, the inertial memristor-based neural networks are originally ISS, by restricting the upper bound of the average impulsive interval, the ISS of inertial memristor-based neural networks with destabilizing impulses can be ensured. Finally, numerical results are presented to illustrate the main results.
Keywords:Corresponding author at: Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing  Department of Electronics and Information Engineering  Southwest University  Chongqing 400715  China  
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