Stability analysis of fuzzy Markovian jumping Cohen–Grossberg BAM neural networks with mixed time-varying delays

Sathy, R.; Balasubramaniam, P.

doi:10.1016/j.cnsns.2010.08.012

Cited by 45 publications

(8 citation statements)

References 38 publications

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“…It is a cornerstone in image processing and pattern recognition. Recently, some results on stability and other behaviors have been derived for fuzzy neural networks with or without time delays (see [14][15][16][17][18][19][20][21][22]).…”

Section: Introductionmentioning

confidence: 99%

Almost Automorphic Solutions for Fuzzy Cohen-Grossberg Neural Networks with Mixed Time Delays

Meng

Zhang

2015

Mathematical Problems in Engineering

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This paper is concerned with the problem of almost automorphic solutions of a class of fuzzy Cohen-Grossberg neural networks with mixed time delays and variable coefficients. Based on inequality analysis techniques and combining the exponential dichotomy with fixed point theorem, some sufficient conditions for the existence and global exponential stability of almost automorphic solutions are obtained. Finally, a numerical example is given to show the feasibility of our results.

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Section: Introductionmentioning

confidence: 99%

Almost Automorphic Solutions for Fuzzy Cohen-Grossberg Neural Networks with Mixed Time Delays

Meng

Zhang

2015

Mathematical Problems in Engineering

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“…Therefore, the equilibrium and stability properties of NNs with time delay have been widely considered by many researchers. Up to now, various stability conditions have been obtained, and many excellent papers and monographs have been available (see [1][2][3][4][5][6][7][8]). So far, these obtained stability results are classified into two types: delay independent and delay dependent.…”

Section: Introductionmentioning

confidence: 99%

Piecewise Convex Technique for the Stability Analysis of Delayed Neural Network

Liu

et al. 2013

Journal of Applied Mathematics

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On the basis of the fact that the neuron activation function is sector bounded, this paper transforms the researched original delayed neural network into a linear uncertain system. Combined with delay partitioning technique, by using the convex combination between decomposed time delay and positive matrix, this paper constructs a novel Lyapunov function to derive new less conservative stability criteria. The benefit of the method used in this paper is that it can utilize more information on slope of the activations and time delays. To illustrate the effectiveness of the new established stable criteria, one numerical example and an application example are proposed to compare with some recent results.

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“…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.…”

Section: Corollary 13 Under Assumption (A) For Given Constantsmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Yogambigai

Ali

Zhu

et al. 2018

Mathematical Problems in Engineering

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The problem of exponential Lagrange stability analysis of Markovian jump neural networks with leakage delay and mixed timevarying delays is studied in this paper. By utilizing the Lyapunov functional method, employing free-weighting matrix approach and inequality techniques in matrix form, we establish several novel stability criteria such that, for all admissible parameter uncertainties, the suggested neural network is exponentially stable in Lagrange sense. The derived criteria are expressed in terms of linear matrix inequalities (LMIs). A numerical example is provided to manifest the validity of the proposed results.

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Stability analysis of fuzzy Markovian jumping Cohen–Grossberg BAM neural networks with mixed time-varying delays

Cited by 45 publications

References 38 publications

Almost Automorphic Solutions for Fuzzy Cohen-Grossberg Neural Networks with Mixed Time Delays

Almost Automorphic Solutions for Fuzzy Cohen-Grossberg Neural Networks with Mixed Time Delays

Piecewise Convex Technique for the Stability Analysis of Delayed Neural Network

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

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