Passivity of memristor-based BAM neural networks with different memductance and uncertain delays

Abstract

This paper addresses the passivity problem for a class of memristor-based bidirectional associate memory (BAM) neural networks with uncertain time-varying delays. In particular, the proposed memristive BAM neural networks is formulated with two different types of memductance functions. By constructing proper Lyapunov-Krasovskii functional and using differential inclusions theory, a new set of sufficient condition is obtained in terms of linear matrix inequalities which guarantee the passivity criteria for the considered neural networks. Finally, two numerical examples are given to illustrate the effectiveness of the proposed theoretical results.

Dynamics of BAM neural networks with mixed delays and leakage time‐varying delays in the weighted pseudo–almost periodic on time‐space scales

“…During the last few decades, there had emerged a considerable interest in the study of neural network dynamics due to their successful application to various fields such as signal processing, associative memories, and pattern recognition among others. [9][10][11][12][13][14][15][16][17] Various models of neural networks, such as cellular neural networks, 1,6 recurrent neural networks, 2,18,19 and bidirectional associative memory (BAM) neural networks, [20][21][22][23][24][25][26] have been extensively investigated. Especially the first model of BAM neural networks was introduced in 1988 by Kosto.…”

Stability of impulsive coupled systems on networks with both multicoupling structure and time‐varying delays

“…In this section, we take BAM neural networks as an example to show CSNs with multicoupling structure. In recent years, BAM neural networks are investigated extensively because they show more excellent characteristics and wide applications in science and engineering fields . Consider the following delayed BAM neural network: where u k ( t ) denotes the state variable related to the k th neuron; a k , b k are positive constants; c kh and d kh are constants and denote the immediate coupling strength and delayed coupling strength of the neural networks, respectively; I k is the external input; and denotes the neuron activation function of the k th neuron.…”

Exponential Lagrange Stability for Markovian Jump Uncertain Neural Networks with Leakage Delay and Mixed Time-Varying Delays via Impulsive Control

“…In this paper we studied exponential Lagrange stability for Markovian jump uncertain neural networks with leakage delay and mixed time-varying delays via impulsive control. The stability of neural networks was studied by applying the differential inequality and Lyapunov method [30][31][32][33][34]. And the authors considered Lyapunov stability, where the Lyapunov stability of equilibrium point can be regarded as a special case of the Lagrange stability.…”

“…Noting that while signal propagation is sometimes instantaneous and can be modeled with discrete delays, it may also be distributed during a certain time period so that distributed delays are incorporated into the model [29]. Thus, the issue of stability analysis for neural framework with time-varying delays is investigated via LMI technique by many authors [30][31][32][33][34]. Very recently, a leakage delay, which is the time delay in leakage term of the systems and a considerable factor affecting dynamics for the worse in the systems, has been applied to use in studying the problem of stability for neural networks [35][36][37].…”