This article identifies a new upper bound norm for the intervalized interconnection matrices pertaining to delayed dynamical neural networks under the parameter uncertainties. By formulating the appropriate Lyapunov functional and slope-bounded activation functions, the derived new upper bound norms provide new sufficient conditions corresponding to the equilibrium point of the globally asymptotic robust stability with respect to the delayed neural networks. The new upper bound norm also yields the optimized minimum results as compared with some existing methods. Numerical examples are given to demonstrate the effectiveness of the proposed results obtained through the new upper bound norm method.

In this paper, we focus on the global stability analysis with respect to dynamical delayed neural network (NN) models that contain parameter uncertainties. Many investigations on the sufficient conditions utilizing different upper bounds for the norm of interconnection matrices pertaining to the global asymptotic robust stability of delayed NN models have been conducted. In this study, a new upper bound of the norm of connection weight matrices is derived for the delayed NN models under parameter uncertainties. The key focus is on how the new upper bound is able to yield minimum result with respects to some of the existing upper bounds.We demonstrate that the new upper bound can lead to some new sufficient conditions with respect to the global asymptotic robust stability of equilibrium point of the delayed NN models. The slope bounded activation functions and Lyapunov-Krasovskii functionals are employed for formulating the sufficient conditions of the NN equilibrium point. Moreover, the derived sufficient conditions are independent on the time delay parameter. Numerical examples are provided, and the outcomes obtained are compared with those of the existing results subject to different network parameters.

In this paper, we study some new sufficient criteria on global stability analysis for the hybrid bidirectional associative memory (BAM) neural networks with multiple time delays. The ultimate focus of this paper is to derive some new generalized sufficient criteria for the global asymptotic robust stability (GARS) of equilibrium point of the time-delayed BAM neural networks. The obtained sufficient conditions are always independent on the delay of system parameters of hybrid BAM neural networks. Finally, numerical example has been given to explain the effectiveness of our results in terms of network parameters.

The present research investigates the global asymptotic stability of bidirectional associative memory (BAM) neural networks using distinct sufficient conditions. The primary objective of this study is to establish new generalized criteria for the global asymptotic robust stability of time-delayed BAM neural networks at the equilibrium point, utilizing the Frobenius norm and the positive symmetrical approach. The new sufficient conditions are derived with the help of the Lyapunov–Krasovskii functional and the Frobenius norm, which are important in deep learning for a variety of reasons. The derived conditions are not influenced by the system parameter delays of the BAM neural network. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed conclusions regarding network parameters.

This study investigates the global asymptotic stability of hybrid bidirectional associative memory (BAM) complex-valued neural networks (CVNNs) with time-varying delays and uncertain parameters, where the system matrices are assumed to be symmetric. By constructing an appropriate Lyapunov–Krasovskii functional (LKF), new sufficient conditions are derived to guarantee the existence and uniqueness of equilibrium points, as well as to establish the global asymptotic stability of the proposed symmetric hybrid BAM CVNNs. The validity and effectiveness of the theoretical results are further demonstrated through detailed numerical examples.