随机时滞BAM神经网络的全局散逸性

把不确定性因素考虑到双向联想记忆神经网络(BAM)中, 得到一类带Brown运动的随机时滞双向联想记忆神经网络(BAM)模型. 在激活函数有界的条件下, 研究了随机时滞BAM神经网络的全局散逸性. 通过Lyapunov泛函、Jensen不等式和It 公式等, 讨论了随机时滞BAM神经网络系统均方散逸的充分条件, 给出了该系统散逸的吸引集. 通过数值例子对所给出的结论进行了验证.

Global Dissipativity for Stochastic BAM Neural Networks with Time Delay

Considering the randomness, which is one of the uncertain factors in the bidirectional associative memory(BAM) neural networks system, it is obtained that a class of stochastic bidirectional associative memory(BAM) neural networks with time delay and Brownian motion. Under the condition of the bounded activation function of the equation, it discusses the global dissipativity for stochastic bidirectional associative memory (BAM) networks with time delay. By using Lyapunov functions, Jensen's inequality and It's formula,it provides the sufficient condition for the global dissipativity in the mean square of such stochastic bidirectional associative memory (BAM) neural networks;it also gives the attractive set of the system. Finally, the numerical example is provided to demonstrate the effectiveness of the conclusion. The conclusion is a generalization of the existing literature in the paper.