The robust exponential stability is investigated for a class of uncertain neutral-type neural networks with discrete and distributed time-varying delays. By introducing a new vector Lyapunov-Krasovskii functional, using Jensen integral inequality, free-weighting matrix method and linear matrix inequality techniques, delay-dependent sufficient conditions are obtained for exponential stability of...

In real-world systems, interactions between elements do not happen instantaneously, due to the time required for a signal to propagate, reaction times of individual elements, and so forth. Moreover, time delays are normally nonconstant and may vary with time. This means that it is vital to introduce time delays in any realistic model of neural networks. In order to analyze the fundamental prope...

This research investigates the synchronization of distributed delayed discrete-time fractional-order complex-valued neural networks. The necessary conditions have been established for stability proposed networks using theory discrete fractional calculus, Laplace transform, and Mittag–Leffler functions. In order to guarantee global asymptotic stability, adequate criteria are determined Lyapunov’...

This paper investigates the robust stabilization of discrete-time systems with time-varying input delays and model uncertainties by predictor-based anti-disturbance output-feedback control strategies. Here, a novel predictor-feedback combined an extended state observer is proposed. The objective to counteract negative effects while actively rejecting disturbance signals typically encountered in...

For a class of discrete-time bidirectional associative memory neural networks (DTBAMNNs) with multiple time-varying delays, the issue state estimation is studied. By propose mathematical induction method, we first investigate novel delay-dependent and -independent global exponential stability (GES) criteria error system. The obtained GES are described by linear scalar inequalities. Then, observ...