Journal
CANADIAN JOURNAL OF PHYSICS
Volume 93, Issue 4, Pages 398-408Publisher
CANADIAN SCIENCE PUBLISHING, NRC RESEARCH PRESS
DOI: 10.1139/cjp-2014-0264
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Funding
- National Research Foundation of Korea (NRF) - Ministry of Education, Science and Technology [2011-0009273]
- Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) - Korea government Ministry of Trade, Industry and Energy [20144030200450]
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This paper proposes new delay-dependent stability criteria for discrete-time neural networks with interval time-varying delays and probabilistic occurring parameter uncertainties. It is assumed that parameter uncertainties are changed with the environment, explored using random situations, and its stochastic information is included in the proposed method. By constructing a suitable Lyapunov-Krasovskii functional, new delay-dependent stability criteria for the concerned systems are established in terms of linear matrix inequalities, which can be easily solved by various effective optimization algorithms. Two numerical examples are given to illustrate the effectiveness of the proposed method.
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