时滞标准神经网络模型及其应用

提出一种新的神经网络模型---时滞标准神经网络模型(DSNNM),它由线性动力学系统和有界静态时滞非线性算子连接而成.利用不同的Lyapunov泛函和S方法推导出DSNNM全局渐近稳定性和全局指数稳定性的充分条件,这些条件可表示为线性不等式(LMI)形式.大多数时滞(或非时滞)动态神经网络(DANN)稳定性分析或神经网络控制系统都可以转化为DSNNM,以便用统一的方法进行稳定性分析或镇定控制.从DSNNM应用于时滞联想记忆(BAM)神经网络的稳定性分析以及PH中和过程神经控制器的综合实例,可以看出,得到的稳定性判据扩展并改进了以往文献中的稳定性定理,而且可将稳定性分析推广到非线性控制系统的综合.

Delayed Standard Neural Network Model and Its Application

A novel neural network model,named delayed standard neural network model (DSNNM),is proposed,which is the interconnection of a linear dynamic system and a bounded static delayed nonlinear operator.By combining a number of different Lyapunov functionals with S-Procedure,some sufficient conditions for global asymptotic stability and global exponential stability of the DSNNM are derived and formulated as linear matrix in- equalities(LMIs).Most delayed(or non-delayed)dynamic artificial neural networks(DANNs) or neuro-control systems can be transformed into DSNNMs so that stability analysis or sta- bilization synthesis can be done in a unified way.In this paper,DSNNMs are applied to analyzing the stability of the delayed bidirectional associative memory(BAM)neural net- works and synthesizing the neuro-controllers for the PH neutralization process.The stability criteria obtained turn out to be a generalization of some previous criteria.The analysis ap- proach is further extended to the nonlinear control system.