【摘要】 研究具分布参数的Cohen Grossberg广义随机神经网络的镇定性。将所考虑的系统的解随机场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镇定性。具体实施方法是运用It 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,从而克服了研究具分布参数随机系统无相应It 公式的困难。更多还原

【Abstract】 This paper studies stabilization of Cohen-Grossberg type generalized neural networks with distributed parameters. The main idea is to treat the integral of the stochastic field solution to the system as the solution process to the corresponding stochastic ordinary differential equation and then discuss stabilization of the solution process. The integral is respect to the spatial variables. To implement this idea,the Itō differential formula is used to compute the differential of a Lyapunov function along the system. The Lyapunov function is the average respect to the spatial variables.It is found that there has been no Itō formula for the stochastic systems with distributed parameters up to now,the difficulty of which has been overcome by the proposed method.更多还原