title: Improved delay-dependent robust stability criteria for recurrent neural networks with time-varying delays.
abstract: In this paper, the problem of improved delay-dependent robust stability criteria for recurrent neural networks (RNNs) with time-varying delays is investigated. Combining the Lyapunov-Krasovskii functional with linear matrix inequality (LMI) techniques and integral inequality approach (IIA), delay-dependent robust stability conditions for RNNs with time-varying delay, expressed in terms of quadratic forms of state and LMI, are derived. The proposed methods contain the least numbers of computed variables while maintaining the effectiveness of the stability conditions. Both theoretical and numerical comparisons have been provided to show the effectiveness and efficiency of the present method. Numerical examples are included to show that the proposed method is effective and can provide less conservative results.
authors.affiliation: Chienkuo Technology University, Department of Automation Engineering Institute of Mechatronoptic Systems, 1 Chien-Shous N. Load, Changhua, 500 Taiwan, ROC. lpl@cc.ctu.edu.tw
authors.forename: Pin-Lin
authors.initials: PL
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer