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Nonlinear Dynamics

, Volume 79, Issue 1, pp 83–91 | Cite as

Further results on dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties

  • Hong-Bing Zeng
  • Ju H. Park
  • Jian-Wei Xia
Original Paper

Abstract

In this paper, the problem of robust dissipativity is investigated for neural networks with both time-varying delay and randomly occurring uncertainties. The randomly occurring uncertainties are assumed to obey mutually uncorrelated Bernoulli-distributed white noise sequences. By constructing a new Lyapunov–Krasovskii functional, some improved delay-dependent dissipativity conditions are derived based on two integral inequalities, which are formulated in terms of linear matrix inequality. Furthermore, some information of activation function ignored in previous works has been taken into account in the resulting condition. The effectiveness and the improvement of the proposed approach are demonstrated by two illustrating numerical examples.

Keywords

Neural networks Time delay Randomly occurring uncertainties Dissipativity 

Notes

Acknowledgments

This work of H.B. Zeng was supported by the National Natural Science Foundation of China under Grant Nos. 61304064 and 61273157. The work of J.H. Park was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2013R1A1A2A10005201). Special thanks of Dr. J.H. Park go to N. Kim for continuous supports and encouragement on his works.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Electrical and Information EngineeringHunan University of TechnologyZhuzhouChina
  2. 2.Department of Electrical EngineeringYeungnam UniversityKyongsanRepublic of Korea
  3. 3.School of Mathematic ScienceLiaocheng UniversityLiaochengChina

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