Stability analysis of static recurrent neural networks using delay-partitioning and projection.
This paper introduces an effective approach to studying the stability of recurrent neural networks with a time-invariant delay. By employing a new Lyapunov-Krasovskii functional form based on delay partitioning, novel delay-dependent stability criteria are established to guarantee the global asymptotic stability of static neural networks. These conditions are expressed in the framework of linear matrix inequalities, which can be verified easily by means of standard software. It is shown, by comparing with existing approaches, that the delay-partitioning projection approach can largely reduce the conservatism of the stability results. Finally, two examples are given to show the effectiveness of the theoretical results.
Department of Mechanical Engineering, University of Hong Kong, Pokfulam Road, Hong Kong. dubaozhu@gmail.com
Baozhu
B
Du
James
J
Lam
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005544:Forecasting; D016014:Linear Models; D008433:Mathematics; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012984:Software; D013997:Time Factors