Mean square exponential stability for stochastic memristor-based neural networks with leakage delay

Under the framework of Filippov solutions, the issues of mean square exponential stability for stochastic memristor-based neural networks with leakage delay in this paper are studied. By constructing a suitable Lyapunov-Krasovskii functional and using Ito's differential formula, Lemma of Schur complement and linear matrix inequality technique, the criteria are derived. The criteria are formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of the MATLAB toolbox. Compared with previous results, the activation function's boundedness, differentiability and monotonicity are not required. Finally, three numerical examples are provided to illustrate the effectiveness of the proposed results. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates the mean square exponential stability of stochastic memristor-based neural networks with leakage delay under the framework of Filippov solutions. Criteria for stability are established using Lyapunov-Krasovskii functional, Ito's differential formula, Schur complement lemma, and linear matrix inequality technique. The proposed criteria do not require activation function's boundedness, differentiability, and monotonicity, and are efficiently checked using MATLAB toolbox. Three numerical examples are provided to illustrate the effectiveness of the results.