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Multiple Mittag-Leffler stability of fractional-order competitive neural networks with Gaussian activation functions
Pingping Liu1, Xiaobing Nie1, Jinling Liang1
1The Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, and School of Mathematics, Southeast University, Nanjing 211189, China.
Abstract:
In this paper, we explore the coexistence and dynamical behaviors of multiple equilibrium points for fractional-order competitive neural networks with Gaussian activation functions. By virtue of the geometrical properties of activation functions, the fixed point theorem and the theory of fractional-order differential equation, some sufficient conditions are established to guarantee that such n-neuron neural networks have exactly 3k equilibrium points with 0≤k≤n, among which 2k equilibrium points are locally Mittag-Leffler stable. The obtained results cover both multistability and mono-stability of fractional-order neural networks and integer-order neural networks. Two illustrative examples with their computer simulations are presented to verify the theoretical analysis.
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