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 inequalities (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 stabilization 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 networks 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 approach is further extended to the nonlinear control system.