Robust Stability Analysis of Fractional-Order Hopfield Neural Networks with Parameter Uncertainties | Zendy

Shuo Zhang | Zendy; Yongguang Yu | Zendy; Wei Hu | Zendy

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Robust Stability Analysis of Fractional-Order Hopfield Neural Networks with Parameter Uncertainties

Author(s) -

Shuo Zhang,

Yongguang Yu,

Wei Hu

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 - 10.1155/2014/302702

Subject(s) - uniqueness , equilibrium point , artificial neural network , stability (learning theory) , synchronization (alternating current) , lyapunov function , control theory (sociology) , mathematics , lyapunov stability , hopfield network , computer science , topology (electrical circuits) , mathematical analysis , differential equation , artificial intelligence , nonlinear system , physics , control (management) , machine learning , combinatorics , quantum mechanics

The issue of robust stability for fractional-order Hopfield neural networks with parameter uncertainties is investigated in this paper. For such neural system, its existence, uniqueness, and global Mittag-Leffler stability of the equilibrium point are analyzed by employing suitable Lyapunov functionals. Based on the fractional-order Lyapunov direct method, the sufficient conditions are proposed for the robust stability of the studied networks. Moreover, robust synchronization and quasi-synchronization between the class of neural networks are discussed. Furthermore, some numerical examples are given to show the effectiveness of our obtained theoretical results

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