Journal
NONLINEAR ANALYSIS-MODELLING AND CONTROL
Volume 22, Issue 4, Pages 505-520Publisher
INST MATHEMATICS & INFORMATICS
DOI: 10.15388/NA.2017.4.6
Keywords
fractional-order neural network; inverse Lipschitz neuron activations; topological degree theory; stability analysis
Funding
- National Natural Science Foundation of China [61573096, 61272530]
- Natural Science Foundation of Jiangsu Province of China [BK2012741]
- 333 Engineering Foundation of Jiangsu Province of China [BRA2015286]
- Fundamental Research Funds for the Central Universities
- JSPS Innovation Program [KYZZ16_0115]
- Scientific Research Foundation of Graduate School of Southeast University [YBJJ1663]
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At the beginning, a class of fractional-order delayed neural networks were employed. It is known that the active functions in a target model may be Lipschitz continuous, while some others may also possessing inverse Lipschitz properties. Based upon the topological degree theory, nonsmooth analysis, as well as nonlinear measure method, several novel sufficient conditions are established towards the existence as well as uniqueness of the equilibrium point, which are voiced in terms of linear matrix inequalities (LMIs). Furthermore, the stability analysis is also attached. One numerical example and its simulations are presented to illustrate the theoretical findings.
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