Synchronization of Fractional-order Neural Networks via Intermittent Quantized Control: Optimal Algorithm

Abstract

The biggest challenge of this article is how to maximize the rest time of intermittent controllers. This paper mainly uses intermittent quantized controller (IQC) to examine asymptotic synchronization between fractional-order neural networks (FONNs). Firstly, by utilizing the advantages of intermittent properties, a novel lemma with asymptotic stability inequalities is proposed. Secondly, combining intermittent properties with quantization technique, two different categories of aperiodically intermittent quantized controllers (AIQCs) are designed to ensure asymptotic convergence of FONNs. Due to the certain correlation between control interval, rest interval, and convergence rate parameters, thus, optimization algorithm becomes particularly important in maximizing rest time as much as possible. Thirdly, by constructing Lyapunov functions, several useful conditions are established for the asymptotic synchronization of FONNs. Finally, the rationality of the proposed theoretical analysis is confirmed by two numerical examples.