Global Robust Exponential Stability Analysis for Interval Neural Networks with Mixed Delays | Zendy

Yanke Du | Zendy; Rui Xu | Zendy

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Global Robust Exponential Stability Analysis for Interval Neural Networks with Mixed Delays

Author(s) -

Yanke Du,

Rui Xu

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 - 10.1155/2012/647231

Subject(s) - mathematics , uniqueness , homeomorphism (graph theory) , equilibrium point , exponential stability , interval (graph theory) , artificial neural network , class (philosophy) , linear matrix inequality , stability (learning theory) , matrix (chemical analysis) , control theory (sociology) , mathematical optimization , mathematical analysis , differential equation , discrete mathematics , computer science , nonlinear system , combinatorics , control (management) , artificial intelligence , physics , materials science , quantum mechanics , machine learning , composite material

A class of interval neural networks with time-varying delays and distributed delays is investigated. By employing H-matrix and M-matrix theory, homeomorphism techniques, Lyapunov functional method, and linear matrix inequality approach, sufficient conditions for the existence, uniqueness, and global robust exponential stability of the equilibrium point to the neural networks are established and some previously published results are improved and generalized. Finally, some numerical examples are given to illustrate the effectiveness of the theoretical results

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