Dissipativity analysis for neural networks with two-delay components using an extended reciprocally convex matrix inequality

This paper focuses on the problem of strictly (Q,S,R)-γ-dissipativity analysis for neural networks with two-delay components. Based on the dynamic delay interval method, a Lyapunov-Krasovskii functional is constructed. By solving its self-positive definite and derivative negative definite conditions via an extended reciprocally convex matrix inequality, several new sufficient conditions that guarantee the neural networks strictly (Q,S,R)-γ-dissipative are derived. Furthermore, the dissipativity analysis of neural networks with two-delay components is extended to the stability analysis. Finally, two numerical examples are employed to illustrate the advantages of the proposed method.