title: Further improvement on delay-dependent robust stability criteria for neutral-type recurrent neural networks with time-varying delays.
abstract: This paper is concerned with the problem of improved delay-dependent robust stability criteria for neutral-type recurrent neural networks (NRNNs) with time-varying delays. 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 number of computed variables while maintaining the effectiveness of the robust 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: Department of Automation Engineering, Institute of Mechatronoptic System, Chienkuo Technology University, Changhua 500, Taiwan, ROC. Electronic address: lpl@cc.ctu.edu.tw.
authors.forename: Pin-Lin
authors.initials: PL
authors.lastname: Liu
mesh_terms: D000818:Animals; D003198:Computer Simulation; D025461:Feedback, Physiological / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics
keywords: Integral inequality approach (IIA); Linear matrix inequalities (LMIs); Maximum allowable delay bound (MADB); Neutral-type recurrent neural networks (NRNNs)