Chaos and complexity in a fractional-order financial system with time delays

Wang, Shaojie; He, Shaobo; Yousefpour, Amin; Jahanshahi, Hadi; Repnik, Robert; Perc, Matjaž

doi:10.1016/j.chaos.2019.109521

Cited by 106 publications

(44 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When a chaotic system has higher complexity, this means that the system can be chaotic; otherwise, the system is periodic or convergent. As a matter of fact, the results for complexity measure match well with those of the corresponding Lyapunov exponents analysis [33,34]. For networks, there are also generated time series, but there are few articles on the complexity of networks.…”

Section: Introductionsupporting

confidence: 68%

Complexity and Chimera States in a Ring-Coupled Fractional-Order Memristor Neural Network

2020

Front. Appl. Math. Stat.

Self Cite

View full text Add to dashboard Cite

At present, dynamics and coupled control of fractional-order non-linear systems are arousing much interest from researchers. In this paper, the fractional-order derivative is introduced into an improved memristor neural system. The dynamics of the fractional-order memristor neural model are investigated by means of bifurcation diagrams, Lyapunov exponents, and phase diagrams. To discuss the dynamical behavior of a fractional-order memristor neuron in a network, we construct a ring network of neurons and capture the spatiotemporal patterns of the neurons in the network in the presence of different excitations. Finally, the chimera state is observed, and the complexity of the network is analyzed. The analysis shows that the complexity algorithm provides a new approach for the dynamical analysis of the network.

show abstract

Section: Introductionsupporting

confidence: 68%

Complexity and Chimera States in a Ring-Coupled Fractional-Order Memristor Neural Network

2020

Front. Appl. Math. Stat.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Depending on the particular order employed, this memory of past values can correspond to weights of the said past values that vanish for older time instants. This is the reason fractional derivatives are used to model phenomena such as distributions corresponding to power laws, long tails in general, or chaotic systems [25].…”

Section: Gdp Modelsmentioning

confidence: 99%

Fractional Derivatives for Economic Growth Modelling of the Group of Twenty: Application to Prediction

Tejado

Valério

2020

Mathematics

View full text Add to dashboard Cite

This paper studies the economic growth of the countries in the Group of Twenty (G20) in the period 1970–2018. It presents dynamic models for the world’s most important national economies, including for the first time several economies which are not highly developed. Additional care has been devoted to the number of years needed for an accurate short-term prediction of future outputs. Integer order and fractional order differential equation models were obtained from the data. Their output is the gross domestic product (GDP) of a G20 country. Models are multi-input; GDP is found from all or some of the following variables: country’s land area, arable land, population, school attendance, gross capital formation (GCF), exports of goods and services, general government final consumption expenditure (GGFCE), and broad money (M3). Results confirm the better performance of fractional models. This has been established employing several summary statistics. Fractional models do not require increasing the number of parameters, neither do they sacrifice the ability to predict GDP evolution in the short-term. It was found that data over 15 years allows building a model with a satisfactory prediction of the evolution of the GDP.

show abstract

“…More complex multidimensional, multiscroll, and multiwing chaotic systems have also been studied [6][7][8]. e dynamic behaviours of chaotic systems with time-delay have also been studied extensively [9][10][11]. Chaos in many financial systems has also been considered in the literature [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Synchronization of a Hyperchaotic Finance System

Chen

Wang

et al. 2021

Complexity

View full text Add to dashboard Cite

In this article, we propose a series of control strategies to synchronize two chaotic financial systems. Due to the characteristics of chaotic systems, the system is very sensitive to its initial values. Thus, the behaviour of two systems with different initial values will be completely different. In order to realize the synchronization of two financial chaotic systems, we designed a series of controls including controllers to realize global asymptotic synchronization and controllers to realize global exponential synchronization to make the two systems fully synchronized. We provide mathematical proofs to show that the designed controls are effective. Numerical methods are used to verify the effectiveness of the controls.

show abstract

Chaos and complexity in a fractional-order financial system with time delays

Cited by 106 publications

References 19 publications

Complexity and Chimera States in a Ring-Coupled Fractional-Order Memristor Neural Network

Complexity and Chimera States in a Ring-Coupled Fractional-Order Memristor Neural Network

Fractional Derivatives for Economic Growth Modelling of the Group of Twenty: Application to Prediction

Synchronization of a Hyperchaotic Finance System

Contact Info

Product

Resources

About