LAUSR

Abstract This study investigates the exponential stability and dissipativity issues for generalized neural networks with mixed time-varying delayed signals. The objective of the present work is to determine whether the new exponential stability and dissipativity criterions could be established for generalized delayed neural networks or not. A novel Lyapunov–Krasovskii functional that does not require one of terms that is positive definite is firstly constructed based on a peculiar structural matrix. It can be widely used to stability analyst, and better improve the algorithm performance. By employing novel integral inequalities and functional analysis theory, two optimization algorithms are developed. Finally, compared examples are presented to illustrate the effectiveness and the superiority of the proposed methods.