Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations

作者：

Highlights：

• Considering some unpredictable factors of environment, this paper investigates the multistability of MCGNNs with stochastic parameter perturbations for the first time.

• Some sufficient conditions are presented to guarantee exponential multistability of MCGNNs with time-varying delays and parameter perturbations.

• The exponentially stable equilibrium points of MCGNN system can be flexibly located in the odd-sequence or the even-sequence regions. And there exist at least (w+2)l (or (w+1)l) exponentially stable equilibrium points in the odd-sequence (or the even-sequence) regions. This means that the perturbed MCGNN system has potential application value in associative memory storage and secure communication. Therefore, the obtained result can enlarge and strengthen the existing results.

摘要

•Considering some unpredictable factors of environment, this paper investigates the multistability of MCGNNs with stochastic parameter perturbations for the first time.•Some sufficient conditions are presented to guarantee exponential multistability of MCGNNs with time-varying delays and parameter perturbations.•The exponentially stable equilibrium points of MCGNN system can be flexibly located in the odd-sequence or the even-sequence regions. And there exist at least (w+2)l (or (w+1)l) exponentially stable equilibrium points in the odd-sequence (or the even-sequence) regions. This means that the perturbed MCGNN system has potential application value in associative memory storage and secure communication. Therefore, the obtained result can enlarge and strengthen the existing results.

论文关键词：Exponential multistability,Memristive Cohen-Grossberg neural network,Stochastic parameter perturbation,Stable equilibrium point

论文评审过程：Received 18 December 2019, Revised 21 April 2020, Accepted 14 June 2020, Available online 26 June 2020, Version of Record 26 June 2020.

论文官网地址：https://doi.org/10.1016/j.amc.2020.125483