Improved Criteria on Delay-Dependent Stability for Discrete-Time Neural Networks with Interval Time-Varying Delays | Zendy

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

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Improved Criteria on Delay-Dependent Stability for Discrete-Time Neural Networks with Interval Time-Varying Delays

Author(s) -

OhMin Kwon,

Myeongjin Park,

Ju H. Park,

Sangmoon Lee,

E. J.

Publication year - 2012

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2012/285931

Subject(s) - mathematics , interval (graph theory) , stability (learning theory) , control theory (sociology) , artificial neural network , discrete time and continuous time , regular polygon , linear matrix inequality , stability conditions , convex combination , matrix (chemical analysis) , convex optimization , mathematical optimization , computer science , control (management) , statistics , artificial intelligence , materials science , geometry , combinatorics , machine learning , composite material

The purpose of this paper is to investigate the delay-dependent stability analysis for discrete-time neural networks with interval time-varying delays. Based on Lyapunov method, improved delay-dependent criteria for the stability of the networks are derived in terms of linear matrix inequalities (LMIs) by constructing a suitable Lyapunov-Krasovskii functional and utilizing reciprocally convex approach. Also, a new activation condition which has not been considered in the literature is proposed and utilized for derivation of stability criteria. Two numerical examples are given to illustrate the effectiveness of the proposed method

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