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Stability analysis of fractional-order delayed neural networks. (English) Zbl 1420.34096

Summary: At the beginning, a class of fractional-order delayed neural networks is employed. It is known that the activator functions in a target model may be Lipschitz continuous, while some others may also possess 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 by means 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.

MSC:

34K37 Functional-differential equations with fractional derivatives
92B20 Neural networks for/in biological studies, artificial life and related topics
34K20 Stability theory of functional-differential equations
34K21 Stationary solutions of functional-differential equations
47N20 Applications of operator theory to differential and integral equations
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