In this paper, a SEIR epidemic model for the COVID-19 is built according to some general control strategies, such as hospital, quarantine and external input. Based on the data of Hubei province, the particle swarm optimization (PSO) algorithm is applied to estimate the parameters of the system. We found that the parameters of the proposed SEIR model are different for different scenarios. Then, the model is employed to show the evolution of the epidemic in Hubei province, which shows that it can be used to forecast COVID-19 epidemic situation. Moreover, by introducing the seasonality and stochastic infection the parameters, nonlinear dynamics including chaos are found in the system. Finally, we discussed the control strategies of the COVID-19 based on the structure and parameters of the proposed model.

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Since the concept of discrete memristor was proposed, more and more scholars began to study this topic. At present, most of the works on the discrete memristor are devoted to the mathematical modeling and circuit implementation, but the research on its synchronization control has not received much attention. This paper focuses on the parameter identification for the discrete memristive chaotic map, and a modified intelligent optimization algorithm named adaptive differential evolution algorithm is proposed. To deal with the complex behaviors of hyperchaos and coexisting attractors of the considered discrete memristive chaotic maps, the identification objective function adopts two special parts: time sequences and return maps. Numerical simulations demonstrate that the proposed algorithm has the best performance among the six existing algorithms, and it can still accurately identify the parameters of the original system under noise interference.

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Researches on the fracmemristor have aroused increasing interest in the last several years, but there are no reports on design of the discrete fracmemristor. Based on the fractional-order difference and the mathematical model of the charge-controlled memristor, the discrete fracmemristor is designed where the amount of charge is determined by a fractional-order discrete system. In the numerical simulations, it shows that the pinched hysteresis loops are observed, which imply that the proposed memristor satisfies the definition of the memristor. As an application, the fracmemristor sine map is designed, and multistability is observed regarding the initial conditions of both the memristor and system. It provides a potential model for different applications such as cellular neural networks, modulators, sensors, chaotic systems, and programmable digital circuits.

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