title: Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals.
abstract: This article examines the exponential stability analysis problem of generalized neural networks (GNNs) including interval time-varying delayed states. A new improved exponential stability criterion is presented by establishing a proper Lyapunov-Krasovskii functional (LKF) and employing new analysis theory. The improved reciprocally convex combination (RCC) and weighted integral inequality (WII) techniques are utilized to obtain new sufficient conditions to ascertain the exponential stability result of such delayed GNNs. The superiority of the obtained results is clearly demonstrated by numerical examples.
authors.affiliation: Department of Mathematics, Faculty of Science, Maejo University, Sansai 50290, Chiang Mai, Thailand.
authors.forename: G
authors.initials: G
authors.lastname: Rajchakit
authors.affiliation: Department of Mathematics, Faculty of Science, Maejo University, Sansai 50290, Chiang Mai, Thailand.
authors.forename: R
authors.initials: R
authors.lastname: Saravanakumar
authors.affiliation: School of Electrical Engineering, Korea University, Seoul, 136-701, Republic of Korea. Electronic address: hironaka@korea.ac.kr.
authors.forename: Choon Ki
authors.initials: CK
authors.lastname: Ahn
authors.affiliation: Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy.
authors.forename: Hamid Reza
authors.initials: HR
authors.lastname: Karimi
mesh_terms: D003198:Computer Simulation / Q000639:trends; D006801:Humans; D016571:Neural Networks, Computer; D013998:Time Perception
keywords: Generalized neural network; Stability analysis; Time-varying delay; Weighted integral inequality