Abstract
This research work conducts an investigation of the stability issues of neutral-type Cohen–Grossberg neural network models possessing discrete time delays in states and discrete neutral delays in time derivatives of neuron states. By setting a new generalized appropriate Lyapunov functional candidate, some novel sufficient conditions are proposed for global asymptotic stability for the considered neural networks of neutral type. This paper exploits some basic properties of matrices in the derivation of the results that establish a set of algebraic mathematical relationships between network parameters of this neural system. A key feature of the obtained stability criteria is to be independent from time and neutral delays. Therefore, the derived results can be easily tested. Moreover, a constructive numerical example is studied to check the verification of presented global stability conditions.
Highlights
In the past few decades, a variety of neural network models including Hopfield neural networks (HNNs), cellular neural networks (CNNs), Cohen–Grossberg neural networks (CGNNs), and bidirectional associative memory neural networks (BAMNNs) have been utilized for solving some typical engineering problems associated with pattern recognitions, signal processing, associative memories, and optimization related problems [1,2,3,4,5,6,7,8,9]
In the process of electronically implementing a neural network, because of the finite switching speed of operational amplifiers and signal transmission times of neurons due to the communications of neurons, delay parameters encounter. e presence of the time delay parameters may lead to various complex nonlinear dynamics including instability, periodic solutions, and chaos. erefore, one needs to consider the possible effects of these time delays on the stability properties of neural systems
The stability issues for delayed neural networks have been addressed by a variety of researchers, and various sets of novel sufficient results on global asymptotic stability of the equilibrium point for different neural network models have been published [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25]
Summary
Is research work conducts an investigation of the stability issues of neutral-type Cohen–Grossberg neural network models possessing discrete time delays in states and discrete neutral delays in time derivatives of neuron states. By setting a new generalized appropriate Lyapunov functional candidate, some novel sufficient conditions are proposed for global asymptotic stability for the considered neural networks of neutral type. Is paper exploits some basic properties of matrices in the derivation of the results that establish a set of algebraic mathematical relationships between network parameters of this neural system. A key feature of the obtained stability criteria is to be independent from time and neutral delays. Erefore, the derived results can be tested. A constructive numerical example is studied to check the verification of presented global stability conditions
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