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 netwo...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.展开更多
基金supported by the Natural Science Foundation of Shandong Province under Grant No.ZR2014AM006
文摘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.
