具有时滞的双向联想记忆神经网络的鲁棒稳定性

应用线性矩阵不等式技术研究了时滞双向联想记忆神经网络的平衡点稳定性问题.针对存在参数不确定的时滞双向联想记忆神经网络,根据Lyapunov稳定理论,通过构造适当的Lyapunov-Krasovskii泛函,给出了保证双向联想记忆神经网络平衡点全局鲁棒稳定的两个新判据.所得到的结果能够表示成线性矩阵不等式形式,具有易于验证和独立于时变时滞幅值大小等特点.对于慢时变时滞的情况,当时滞幅值较大时,所得结果具有较小的保守性.通过一个仿真例子表明了所得结果的有效性.

Robust Stability of BAM Neural Networks with Delays

Stability problems are studied for the BAM(bi-directional associative memory) neural networks with time-varying delays,based on the linear matrix inequality technique.Using Lyapunov stability theory and constructing a suitable Lyapunov-Krasovskii functional,two new criteria are given to ensure the global robust stability of the equilibrium point in a BAM neural network with uncertainty.As a result in form of linear matrix inequality,it is easy to verify and independent of the magnitude of the time-varying delays.The magnitude becomes greater if the time-varying delay is short,then the result shows less conservative than the delay-dependent stability as shown in earlier works.A simulation example is given to illustrate the effectiveness of the result.