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• W2899664098 abstract "In the present world, due to the complicated dynamic properties of neural cells, many dynamic neural networks are described by neutral functional differential equations including neutral delay differential equations. These neural networks are called neutral neural networks or neural networks of neural-type. The differential expression not only defines the derivative term of the current state but also explains the derivative term of the past state. In this paper, global asymptotic stability of a neutral-type neural networks, with time-varying delays, are presented and analyzed. The neural network is made up of parts that include: linear, non-linear, non-linear delayed, time delays in time derivative states, as well as a part of activation function with the derivative. Different from prior references, as part of the considered networks, the last part involves an activation function with the derivative rather than multiple delays; that is a new class of neutral neural networks. This paper assumes that the activation functions satisfy the Lipschitz conditions so that the considered system has a unique equilibrium point. By constructing a Lyapunov-Krasovskii-type function and by using a linear matrix inequality analysis technique, a sufficient condition for global asymptotic stability of this neural network has been obtained. Finally, we present a numerical example to show the effectiveness and applicability of the proposed approach." @default.
• W2899664098 created "2018-11-16" @default.
• W2899664098 creator A5056067712 @default.
• W2899664098 date "2018-01-01" @default.
• W2899664098 modified "2023-10-14" @default.
• W2899664098 title "Global Asymptotic Stability for a New Class of Neutral Neural Networks" @default.
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• W2899664098 doi "https://doi.org/10.11648/j.ijamtp.20180403.13" @default.
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