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Exponential periodic attractor of impulsive Hopfield-type neural network system with piecewise constant argument

机译:具有分段常数参数的脉冲Hopfield型神经网络系统的指数周期吸引子

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In this paper we study a periodic impulsive Hopfield-type neural network system with piecewise constant argument of generalized type. Under general conditions, existence and uniqueness of solutions of such systems are established using ergodicity, Green functions and Gronwall integral inequality. Some sufficient conditions for the existence and stability of periodic solutions are shown and a new stability criterion based on linear approximation is proposed. Examples with constant and nonconstant coefficients are simulated, illustrating the effectiveness of the results.
机译:在本文中,我们研究了具有广义类型分段常数参数的周期脉冲Hopfield型神经网络系统。在一般条件下,使用遍历性,格林函数和Gronwall积分不等式来确定此类系统解的存在性和唯一性。给出了周期解存在性和稳定性的充分条件,并提出了基于线性逼近的新稳定性准则。模拟了具有恒定和非恒定系数的示例,说明了结果的有效性。

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