Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 9, Issue 4, Pages 599-609Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-016-0547-6
Keywords
Neural networks; Lagrange exponential stability; Neutral; Time delay
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Funding
- National Natural Science Foundation of China [61174216]
- Scientific and Technological Research Program of Chongqing Municipal Education Commission [KJ1501002, KJ1401003]
- Youth Fund of Chongqing Three Gorges University [14QN22]
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In this paper, the global exponential stability in Lagrange sense for neutral type neural networks with mixed time-varying delays is studied. By constructing proper Lyapunov functions and using inequality techniques, new delay-dependent succinct criteria are derived to ensure the global exponential Lagrange stability for the aforementioned neural networks. Meanwhile, globally exponentially attractive sets are given out. The results obtained here are more general than some of existing results. Finally, two examples are presented and analyzed to validate our results.
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