Delay-partitioning approach to stability analysis of state estimation for neutral-type neural networks with both time-varying delays and leakage term via sampled-data control. Issue 8 (11th June 2017)

• Record Type: Journal Article
• Authors: Samidurai, R.; Rajavel, S.; Cao, Jinde; Alsaedi, Ahmad; Alsaadi, Fuad; Ahmad, Bashir
• Is Part Of: International journal of systems science. Volume 48:Issue 8(2017)
• Page Start: 1752
• Page End: 1765
• Publication Date: 2017-06-11
• Subjects: Neural networks -- state estimator -- sampled-data -- leakage delay -- Lyapunov–Krasovskii functional -- time-varying delay; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2017.1282060
• Languages: English
• ISSNs: 0020-7721

Abstract

ABSTRACT: This paper mainly focuses on further improved stability analysis of state estimation for neutral-type neural networks with both time-varying delays and leakage delay via sampled-data control by delay-partitioning approach. Instead of the continuous measurement, the sampled measurement is used to estimate the neuron states and a sampled-data estimator is constructed. To fully use the sawtooth structure characteristics of the sampling input delay, sufficient conditions are derived such that the system governing the error dynamics is asymptotically stable. The design method of the desired state estimator is proposed. We construct a suitable Lyapunov–Krasovskii functional (LKF) with triple and quadruple integral terms then by using a novel free-matrix-based integral inequality (FMII) including well-known integral inequalities as special cases. Moreover, the design procedure can be easily achieved by solving a set of linear matrix inequalities (LMIs), which can be easily facilitated by using the standard numerical software. Finally, two numerical examples are given to demonstrate the effectiveness of the proposed results.