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Robust Stability of a Class of Fractional Order Hopfield Neural Networks

Robust Stability of a Class of Fractional Order Hopfield Neural Networks
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摘要 As the theory of the fractional order differential equation becomes mature gradually, the fractional order neural networks become a new hotspot.The robust stability of a class of fractional order Hopfield neural network with the Caputo derivative is investigated in this paper. The sufficient conditions to guarantee the robust stability of the fractional order Hopfield neural networks are derived by making use of the property of the Mittag-Leffler function, comparison theorem for the fractional order system, and method of the Laplace integral transform. Furthermore, a numerical simulation example is given to illustrate the correctness and effectiveness of our results. As the theory of the fractional order differential equation becomes mature gradually, the fractional order neural networks become a new hotspot.The robust stability of a class of fractional order Hopfield neural network with the Caputo derivative is investigated in this paper. The sufficient conditions to guarantee the robust stability of the fractional order Hopfield neural networks are derived by making use of the property of the Mittag-Leffler function, comparison theorem for the fractional order system, and method of the Laplace integral transform. Furthermore, a numerical simulation example is given to illustrate the correctness and effectiveness of our results.
出处 《Journal of Electronic Science and Technology》 CAS CSCD 2015年第2期153-157,共5页 电子科技学刊(英文版)
基金 supported by the Natural Science Foundation of Shandong Province under Grant No.ZR2014AM006
关键词 fractional Laplace guarantee hotspot mature derivative illustrate Liouville correctness asymptotic fractional Laplace guarantee hotspot mature derivative illustrate Liouville correctness asymptotic
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参考文献12

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