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Global Mittag-Leffler stability of complex valued fractional-order neural network with discrete and distributed delays. (English) Zbl 1354.92005

Summary: Fractional-order Hopfield neural network are often used to model the processing of information on the basis of interaction among the neurons. To show the constancy of the processed information, the system needs to be stable. In this paper, we deal with the problem of existence and uniform stability analysis of a complex valued fractional order delayed neural network. Moreover, as an extension to real valued neural network, this paper provides sufficient conditions for Mittag-Leffler stability of the system. At the end, we give three suitable examples to substantiate the effectiveness of the obtained theoretical results.

MSC:

92B20 Neural networks for/in biological studies, artificial life and related topics
26A33 Fractional derivatives and integrals
35B35 Stability in context of PDEs
34A12 Initial value problems, existence, uniqueness, continuous dependence and continuation of solutions to ordinary differential equations
33E12 Mittag-Leffler functions and generalizations
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