Journal
NEUROCOMPUTING
Volume 142, Issue -, Pages 494-498Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2014.03.047
Keywords
Neural networks; Fractional order; Uniform stability
Categories
Funding
- National Natural Science Foundation of China [61272530, 11072059]
- Natural Science Foundation of Jiangsu Province of China [BK2012741]
- Specialized Research Fund for the Doctoral Program of Higher Education [20110092110017, 20130092110017]
- Research and Innovation Project for College Graduates of Jiangsu Province [CX2Z12_0080]
- Natural Science Foundation of the Jiangsu Higher Education Institutions of China [13KJB110011]
- Scientific Research Foundation of Nanjing Institute of Technology [CKJB2010028]
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This paper investigates a general class of neural networks with a fractional-order derivative. By using the contraction mapping principle, Krasnoselskii fixed point theorem and the inequality technique, some new sufficient conditions are established to ensure the existence and uniqueness of the nontrivial solution. Moreover, uniform stability of the fractional-order neural networks is proposed in fixed time-intervals. Finally, some examples are given to illustrate the effectiveness of theoretical results. (C) 2014 Elsevier B.V. All rights reserved.
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