LMI-Based Stability Criteria for Discrete-Time Neural Networks with Multiple Delays | Zendy

Hui Xu | Zendy; Ranchao Wu | Zendy

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LMI-Based Stability Criteria for Discrete-Time Neural Networks with Multiple Delays

Author(s) -

Hui Xu,

Ranchao Wu

Publication year - 2013

Publication title -

advances in mathematical physics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.283

H-Index - 23

eISSN - 1687-9139

pISSN - 1687-9120

DOI - 10.1155/2013/732406

Subject(s) - discrete time and continuous time , artificial neural network , equilibrium point , control theory (sociology) , stability (learning theory) , exponential stability , sign (mathematics) , connection (principal bundle) , computer science , mathematics , linear matrix inequality , lyapunov function , mathematical optimization , nonlinear system , mathematical analysis , artificial intelligence , differential equation , geometry , statistics , physics , control (management) , quantum mechanics , machine learning

Discrete neural models are of great importance in numerical simulations and practical implementations. In the current paper, a discrete model of continuous-time neural networks with variable and distributed delays is investigated. By Lyapunov stability theory and techniques such as linear matrix inequalities, sufficient conditions guaranteeing the existence and global exponential stability of the unique equilibrium point are obtained. Introduction of LMIs enables one to take into consideration the sign of connection weights. To show the effectiveness of the method, an illustrative example, along with numerical simulation, is presented

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