Abstract:In order to solve the problem of dissipativity for memrisor-based neural networks with time-varying delay,a new method was proposed,which combined a reciprocally convex technique with a Wirtinger-based integral inequality.First,to convert memristive neural networks into the conventional neural networks,differential inclusions and set-valued maps were applied.Then,based on the construction of a Lyapunov-Krasovskii functional with a time-delay coefficient quadratic term of the state vector and a triple integral term,the delay-dependent conditions in terms of linear matrix inequalities were obtained to assure the neural networks strictly dissipative.The derivative of Lyapunov-Krasovskii functional is estimated by using a reciprocally convex technique and a Wirtinger-based integral inequality,which can be easily solved via Matlab.Moreover,the proposed method was extended to investigate the passivity analysis of the considered systems.Finally,the comparisons with the available references showed that this method gives an improvement of 5% in optimal dissipativity performances for various upper bounds of delay variation in numerical examples.In addition,in the same time-delay case,the simulations provided the state trajectories of the neural network system with external input and without external input,respectively.It was shown that the existence of external input was certain to destroy the stability of the system from the simulation results.