Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term | Zendy

Guiying Chen | Zendy; Linshan Wang | Zendy

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Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term

Author(s) -

Guiying Chen,

Linshan Wang

Publication year - 2014

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2014/972608

Subject(s) - artificial neural network , leakage (economics) , exponential stability , term (time) , exponential function , computer science , interval (graph theory) , stability (learning theory) , algorithm , control theory (sociology) , mathematics , artificial intelligence , machine learning , combinatorics , mathematical analysis , physics , macroeconomics , control (management) , nonlinear system , quantum mechanics , economics

The stability of a class of static interval neural networks with time delay in the leakage term is investigated. By using the method of M-matrix and the technique of delay differential inequality, we obtain some sufficient conditions ensuring the global exponential robust stability of the networks. The results in this paper extend the corresponding conclusions without leakage delay. An example is given to illustrate the effectiveness of the obtained results

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