New criteria for dissipativity analysis of Caputo fractional-order neural networks with non-differentiable time-varying delays
Nguyen Thi Phuong, Nguyễn Thị Thanh Huyền, Nguyen Thi Huyen Thu, Nguyen Huu Sau, Mai Viết Thuận · International Journal of Nonlinear Sciences and Numerical Simulation · 2022
Abstract In this article, we investigate the delay-dependent and order-dependent dissipativity analysis for a class of Caputo fractional-order neural networks (FONNs) subject to time-varying delays. By employing the Razumikhin fractional-order (RFO) approach combined with linear matrix inequalities (LMIs) techniques, a new sufficient condition is derived to guarantee that the considered fractional-order is strictly (Q, S, R) − γ − dissipativity. The condition is presented via LMIs and can be efficiently checked. Two numerical examples and simulation results are finally provided to express the effectiveness of the obtained results.