Parameter Identification of Fractional-Order Discrete Chaotic Systems

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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“…x is called inertia weight, which is very important for the search process of PSO algorithm. Here, it is defined by [28] xðtÞ…”

Section: The Pso Algorithmmentioning

confidence: 99%

SEIR modeling of the COVID-19 and its dynamics

2020

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“… ? No Epidemiology He et al ( 2020b ) SEIR 2 1 PSO (Kennedy and Eberhart 1995 ) Unspecified “error” 1 4.000 40 c1 = 2 c2 = 2 w = after Peng et al ( 2019 ) No Epidemiology Hoffman ( 2020 ) SEIR 9 1 PSO (Kennedy and Eberhart 1995 ) ? ?…”

Section: Applications Of Differential Evolution and Particle Swarm Optimization Against Covid-19mentioning

confidence: 99%

Differential evolution and particle swarm optimization against COVID-19

Piotrowski

Piotrowska

2021

Artif Intell Rev

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COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used.

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“…For the Lorenz, Chen, Rössler and other classical continuous chaotic systems, it emerges the differential evolution (DE) algorithm [28,29], particle swarm optimization (PSO) algorithm [30,31], JAYA algorithm [32], artificial bee colony (ABC) algorithm [33], bird swarm algorithm (BSA) [34] to identify the parameters of these systems. In contrast, only a few reports focus on the parameter identification of discrete chaotic maps [35][36][37]. Due to the stronger sensitivity of the parameters in discrete nonlinear systems, it is a difficult challenge to identify the parameters of discrete chaotic maps, especially for the discrete memristive chaotic maps with coexisting attractors (dynamics guided by initial state) [38].…”

Section: Introductionmentioning

confidence: 99%

Parameter identification for discrete memristive chaotic map using adaptive differential evolution algorithm

Peng

Sun

2021

Nonlinear Dyn

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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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Parameter Identification of Fractional-Order Discrete Chaotic Systems

Cited by 50 publications

References 45 publications

SEIR modeling of the COVID-19 and its dynamics

SEIR modeling of the COVID-19 and its dynamics

Differential evolution and particle swarm optimization against COVID-19

Parameter identification for discrete memristive chaotic map using adaptive differential evolution algorithm

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