title: Stability analysis of delayed neural networks via a new integral inequality.
abstract: This paper focuses on stability analysis for neural networks systems with time-varying delays. A more general auxiliary function-based integral inequality is established and some improved delay-dependent stability conditions formulated in terms of linear matrix inequalities (LMIs) are derived by employing a suitable Lyapunov-Krasovskii functional (LKF) and the novel integral inequality. Three well-known application examples are provided to demonstrate the effectiveness and improvements of the proposed method.
authors.affiliation: School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, PR China. Electronic address: yangbin@dlut.edu.cn.
authors.forename: Bin
authors.initials: B
authors.lastname: Yang
authors.affiliation: School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, PR China.
authors.forename: Juan
authors.initials: J
authors.lastname: Wang
authors.affiliation: Department of Computer Science, City University of Hong Kong, Hong Kong 999077, PR China.
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D013997:Time Factors
keywords: Integral inequality; Lyapunov–Krasovskii functional; Neural networks; Stability; Time-varying delay