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Stability of uncertain impulsive stochastic fuzzy neural networks with two additive time delays in the leakage term
by
Li, Jun
, Guo, Liuxiao
, Jin, Yinghua
, Yang, Yongqing
, Hu, Manfeng
in
Artificial Intelligence
/ Computational Biology/Bioinformatics
/ Computational Science and Engineering
/ Computer Science
/ Data Mining and Knowledge Discovery
/ Image Processing and Computer Vision
/ Original Article
/ Probability and Statistics in Computer Science
2015
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Stability of uncertain impulsive stochastic fuzzy neural networks with two additive time delays in the leakage term
by
Li, Jun
, Guo, Liuxiao
, Jin, Yinghua
, Yang, Yongqing
, Hu, Manfeng
in
Artificial Intelligence
/ Computational Biology/Bioinformatics
/ Computational Science and Engineering
/ Computer Science
/ Data Mining and Knowledge Discovery
/ Image Processing and Computer Vision
/ Original Article
/ Probability and Statistics in Computer Science
2015
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Do you wish to request the book?
Stability of uncertain impulsive stochastic fuzzy neural networks with two additive time delays in the leakage term
by
Li, Jun
, Guo, Liuxiao
, Jin, Yinghua
, Yang, Yongqing
, Hu, Manfeng
in
Artificial Intelligence
/ Computational Biology/Bioinformatics
/ Computational Science and Engineering
/ Computer Science
/ Data Mining and Knowledge Discovery
/ Image Processing and Computer Vision
/ Original Article
/ Probability and Statistics in Computer Science
2015
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Stability of uncertain impulsive stochastic fuzzy neural networks with two additive time delays in the leakage term
Journal Article
Stability of uncertain impulsive stochastic fuzzy neural networks with two additive time delays in the leakage term
2015
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Overview
This paper is concerned with the stability problem for a class of impulsive neural networks model, which includes simultaneously parameter uncertainties, stochastic disturbances and two additive time-varying delays in the leakage term. By constructing a suitable Lyapunov–Krasovskii functional that uses the information on the lower and upper bound of the delay sufficiently, a delay-dependent stability criterion is derived by using the free-weighting matrices method for such Takagi–Sugeno fuzzy uncertain impulsive stochastic recurrent neural networks. The obtained conditions are expressed with linear matrix inequalities (LMIs) whose feasibility can be checked easily by MATLAB LMI Control toolbox. Finally, the theoretical result is validated by simulations.
Publisher
Springer London
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