On Less Conservative Stability Criteria for Neural Networks with Time-Varying Delays Utilizing Wirtinger-Based Integral Inequality | Zendy

OhMin Kwon | Zendy; Myeongjin Park | Zendy; Ju H. Park | Zendy; Sangmoon Lee | Zendy; E. J. | Zendy

Open Access

On Less Conservative Stability Criteria for Neural Networks with Time-Varying Delays Utilizing Wirtinger-Based Integral Inequality

Author(s) -

OhMin Kwon,

Myeongjin Park,

Ju H. Park,

Sangmoon Lee,

E. J.

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/859736

Subject(s) - stability (learning theory) , mathematics , artificial neural network , control theory (sociology) , exponential stability , inequality , linear matrix inequality , multiple integral , stability criterion , mathematical optimization , computer science , nonlinear system , mathematical analysis , artificial intelligence , statistics , control (management) , machine learning , physics , quantum mechanics , discrete time and continuous time

This paper investigates the problem of stability analysis for neural networks with time-varying delays. By utilizing the Wirtinger-based integral inequality and constructing a suitable augmented Lyapunov-Krasovskii functional, two less conservative delay-dependent criteria to guarantee the asymptotic stability of the concerned networks are derived in terms of linear matrix inequalities (LMIs). Three numerical examples are included to explain the superiority of the proposed methods by comparing maximum delay bounds with the recent results published in other literature.

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