Abstract
This paper focuses on the impulsive stabilization of fractional-order complex-valued neural networks. Based on impulsive control and some fractional-order differential inequalities, some valid criteria are achieved to ensure the global asymptotic stabilization of the addressed networks. The maximal impulsive strength and the maximal impulsive interval are also given. Under certain conditions, some sufficient conditions are derived to ensure the global $$\alpha $$-exponential stability of the equilibrium point. Compared to the traditional linear feedback control, the impulsive control strategy only needs small control gains and shorter time to achieve global stabilization. When employing the impulsive control to the error system, a parallel criterion regarding to the complete synchronization of the drive-response systems is also generated. The effectiveness and advantages of the proposed methods are confirmed through simulation results.
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