Dissipativity analysis of complex-valued BAM neural networks with time delay

Global Robust Exponential Dissipativity of Uncertain Second-Order BAM Neural Networks With Mixed Time-Varying Delays

This article focuses on the global robust exponential dissipativity (GRED) of uncertain second-order BAM neural networks with mixed time-varying delays. First, a new differential inequality for the concerned second-order system is established. Second, by constructing some new Lyapunov-Krasovskii functionals (LKFs) and applying this new inequality and some other inequalities, some new GRED criteria in the form of linear matrix inequalities are presented. The global exponential attractive sets are also provided simultaneously. Different from the existing reduced-order methods, this article considers some new LKFs to directly analyze the dynamics of the addressed system via a nonreduced-order strategy. Finally, the correctness of the theoretical results is verified by simulation experiments.

Robust Dissipativity Analysis of Hopfield-Type Complex-Valued Neural Networks with Time-Varying Delays and Linear Fractional Uncertainties

We study the robust dissipativity issue with respect to the Hopfield-type of complex-valued neural network (HTCVNN) models incorporated with time-varying delays and linear fractional uncertainties. To avoid the computational issues in the complex domain, we divide the original complex-valued system into two real-valued systems. We devise an appropriate Lyapunov-Krasovskii functional (LKF) equipped with general integral terms to facilitate the analysis. By exploiting the multiple integral inequality method, the sufficient conditions for the dissipativity of HTCVNN models are obtained via the linear matrix inequalities (LMIs). The MATLAB software package is used to solve the LMIs effectively. We devise a number of numerical models and their empirical results positively ascertain the obtained results.