Improved stability analysis of delayed neural networks via Wirtinger-based double integral inequality
Ramasamy Saravanakumar, Muhammed Syed Ali, Grienggrai Rajchakit · 2016
This paper is concerned with the stability analysis of neural networks with time-varying delays using reciprocally convex combination approach and Wirtinger-based double integral inequality. The time-varying delay is need to be bounded and continuous. By constructing suitable Lyapunov-Krasovskii functional (LKF) and introducing appropriate terms in dealing with the positiveness of the LKF, we establish new stability and stabilization criteria in terms of linear matrix inequalities (LMIs). The present method leads to some significant improvements over existing results. A numerical example is given to illustrate the usefulness and effectiveness of the proposed theoretical results.