New Stabilization Method for Delayed Discrete-Time Cohen–Grossberg BAM Neural Networks
Er-yong Cong, Xiao Feng Han, Xian Zhang · IEEE Access · 2020
This paper deals with the state feedback stabilization problem of delayed discrete-time Cohen-Grossberg BAM neural networks. By the mathematical induction method, stabilizable conditions are derived to ensure that the resulting closed-loop system is globally exponentially stable, and thereby, the desired state feedback controller is designed. These stabilizable conditions are very simple, which can easily verified by using the standard toolbox software (for example, MATLAB). The proposed approach is directly based on the definition of global exponential stability, and does not involve the construction of any Lyapunov-Krasovskii functional. For a special case, it is theoretical proven that the proposed method is superior to an existing one. Moreover, several illustrative examples are given to validate the success of the derived theoretical results.