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研究具分布参数的随机Hopfield神经网络的稳定性.主要思想是将所考虑的系统的解关于空间变量的积分视为相应的由随机常微分方程描述的神经网络的解过程来讨论其稳定性,具体实施方法是运用Ito微分公式沿系统对构造的关于空间变量平均的Lyapunov函数进行微分.克服了研究具分布参数随机系统无相应Ito公式的困难.目前文献尚未见有关分布参数随机神经网络的稳定与镇定的相应结果.
The stability of stochastic Hopfield neural networks with distributed parameters is studied.The main idea is to discuss the stability of the neural network which is described by the stochastic ordinary differential equations, The method is to differentiate the constructed Lyapunov function of the average of the spatial variables by using the Ito differential formula, which overcomes the difficulty of studying the non-corresponding Ito formula with stochastic systems with distributed parameters. At present, no literature has been found about the stability of the stochastic neural network with distributed parameters The corresponding result with calming.