Finite-Time Extended Dissipativity Analysis for Generalized Neural Networks With Discrete and Distributed Time-Varying Delays

This paper investigated the finite-time extended dissipativity for generalized neural networks with discrete and distributed time-varying delays via the improved Lyapunov-Krasovskii functional (LKF). We constructed an appropriate LKF by employing more neural network information and consisting of quadratic functions. By combining the proposed LKF, Jensen’s integral inequality, orthogonal polynomials-based integral inequality, and extended Wirtinger’s integral inequality, new delay-dependent conditions are achieved in the form of linear matrix inequalities (LMIs), which can be verified via MATLAB’s LMI toolbox. In addition, we concentrate on the extended dissipative analysis problem, which is a unified formulation of $\mathcal {L}_{2}-\mathcal {L}_{\infty }$ , $H_{\infty }$ , passivity, and dissipative performance. This paper is less conservative delay bound than some recently published literature by stability criteria. In addition, we presented seven numerical examples to illustrate the effectiveness of the obtained results.