Coexistence and local stability of multiple equilibrium points for fractional-order state-dependent switched competitive neural networks with time-varying delays

This paper investigates the coexistence and local stability of multiple equilibrium points for a class of competitive neural networks with sigmoidal activation functions and time-varying delays, in which fractional-order derivative and state-dependent switching are involved at the same time. Some novel criteria are established to ensure that such n-neuron neural networks can have 5m1 center dot 3m2 total equilibrium points and 3m1 center dot 2m2 locally stable equilibrium points with m1+m2 = n, based on the fixed-point theorem, the definition of equilibrium point in the sense of Filippov, the theory of fractional-order differential equation and Lyapunov function method. The investigation implies that the competitive neural networks with switching can possess greater storage capacity than the ones without switching. Moreover, the obtained results include the multistability results of both fractional-order switched Hopfield neural networks and integer-order switched Hopfield neural networks as special cases, thus generalizing and improving some existing works. Finally, four numerical examples are presented to substantiate the effectiveness of the theoretical analysis.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates the coexistence and local stability of multiple equilibrium points for a class of competitive neural networks with sigmoidal activation functions and time-varying delays, in which both fractional-order derivative and state-dependent switching are involved. Novel criteria are established to ensure the existence of equilibrium points and their local stability. The investigation reveals that competitive neural networks with switching can have greater storage capacity than those without switching. The results generalize and improve existing works by including both fractional-order and integer-order switched Hopfield neural networks as special cases. Numerical examples are presented to validate the theoretical analysis.