Fractional-order Financial System Research Articles

Chaotic Dynamic Behavior of a Fractional-Order Financial System with Constant Inelastic Demand

The establishment of a financial system should not only consider the current situation, but also need to refer to the past. Due to the memory of the fractional derivative, a fractional-order system can more effectively describe the historical significance of the financial system. Most scholars use the prediction–correction scheme to study fractional-order systems. This paper provides a higher-precision numerical method for the financial system, which more effectively simulate the system. Based on the definition of the Grünwald–Letnikov fractional derivative, the integer-order system with nonconstant demand elasticity is extended to the fractional-order setting, and its dynamic behavior is studied, with some novel chaotic attractors found. The research results are helpful for improving the understanding of the financial system and the financial market and for predicting financial risks.

A numerical study on fractional order financial system with chaotic and Lyapunov stability analysis

In the last few decades, academic research has focused more on financial problems and poverty levels. These are among the two major challenges of the modern world today. To understand the challenge of financial crisis and poverty in societies. This paper explains a deterministic financial system of non linear differential equations. Which aim to provide recommendations to address the twin challenges. In this fractional chaotic model, memory and chaos are portrayed as effects on a system. Financial systems of fractional order have three compartments: interest rate x(t), investment demand rate y(t) and price indexes rate z(t). For the first time, the Legendre wavelet approximation is applied to fractional order financial systems. A major objective of this process is to convert fractional differential equations (FDEs) into algebraic equations by using Legendre wavelets and their fractional integral operators. For residual analysis, convergence and stability analysis, we have used iteration methods and Legendre wavelets. This study indicates good results between the approximate and numerical solutions and that this method is efficient and accurate. Using the iteration method, we prove that the solution exists, while the contraction theory proves its uniqueness. The findings of this study will be extremely useful for future financial management. We have demonstrated the applicability and effectiveness of these methods by analysing numerical simulations for the fractional order financial model and the numerical simulation has been calculated by MATLAB programming.

Parameter estimation for fractional-order nonlinear systems based on improved sparrow search algorithm

Parameter estimation is important in the study of control and synchronization of fractional-order nonlinear systems (FONSs). This paper proposes an improved Sparrow Search Algorithm (ISSA) for the parameter estimation problem of FONSs. The algorithm improves the population initialization, position update method of discoverers and warning sparrows based on Sparrow Search Algorithm (SSA), and the parameter estimation simulation experiment for fractional-order financial nonlinear system and fractional-order L nonlinear system is conducted to demonstrate this method. The experimental results show that the proposed ISSA is superior to the SSA, Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) and Harris Hawks Optimization (HHO) in terms of parameter optimization accuracy and convergence speed, which validates the advantages of the ISSA.

Investigation of the hyperchaos and control in the fractional order financial system with profit margin

This research study proposes a novel hyperchaotic finance system with profit margin and then utilizes the Adomian Decomposition Method (ADM) to tackle the solution of the associated Caputo-derivative based fractional-order hyperchaotic finance system with profit margin. Numerical simulations and analyses, such as the evaluation of Lyapunov exponents (LE), the creation of bifurcation diagrams (BD), the complexity analysis (CA), and the 0-1 test, are used to get a full picture of the system. The results of our study demonstrate the occurrence of periodic, chaotic, and hyperchaotic dynamics in the system. Furthermore, the overall criteria for the existence and the uniqueness of the exact solutions for Caputo fractional-order models are presented. In addition, we present a control methodology for the fractional-order hyperchaotic financial system utilizing the Laplace transform and linear feedback control. Significantly, our research showcases a noteworthy correlation between the analytical results and numerical simulations, emphasizing the soundness and effectiveness of our methodology.

Dynamic analysis of a new 4D fractional-order financial system and its finite-time fractional integral sliding mode control based on RBF neural network

In this paper, considering the combined effect of interest rate and investment demand on the price index, a new 4D fractional-order financial system is established. Based on the local stability theory, the stability of equilibrium points and static bifurcation phenomena are discussed. Subsequently, to further analyze the dynamic behaviors of the new financial system, numerical tools such as bifurcation diagrams, Lyapunov exponents, phase diagrams, 0–1 test diagrams, and complexity diagrams are employed. It is found that, for a suitable selection of the differential order or system parameter, the proposed system can generate periodic oscillations with different period and chaotic attractors with different shapes. Furthermore, a new controller combining radial basis function (RBF) neural network with fractional-order integral sliding mode control is proposed to realize the finite-time synchronization for a class of fractional-order systems. Finally, the effectiveness of the presented control scheme is verified by numerical simulations and the influence of RBF neural network parameters on the control performance is analyzed.

Bernoulli polynomial based wavelets method for solving chaotic behaviour of financial model

This paper presents an algorithm for solving systems of non integer financial chaotic model. The Bernoulli wavelets function approximation applies to fractional order financial systems for the first time. The main objective of this process is to convert fractional differential equation systems into algebraic equation systems using wavelets approximation based on Bernoulli wavelets and their fractional integral operators. This fractional financial chaotic model that represents the effects of memory and chaos in a system. There are 3 compartments in this fractional order financial system: price indexes, interest rates, and investment demand. Analyses are depends on iteration method and Bernoulli wavelets method to determine the stability analysis, residual error and convergence analysis of the solutions. This study indicates that the presented approximate solutions fit exactly with the numerical solution and that the technique is accurate and effective. Our proposed model has been discussed for its uniqueness, boundedness, and non negativity. Its findings are extremely important for dealing with financial issues in the future.

A modified Salp Swarm Algorithm for parameter estimation of fractional-order chaotic systems

For the parameter estimation problem in research related to the fractional-order chaotic systems (FOCSs), a modified optimization algorithm based on Salp Swarm Algorithm (SSA) was developed in this paper. The proposed algorithm introduced several improvements on SSA: adding a grouping step, introducing “betrayal” behavior, and improving the update method of the followers. We applied multiple classical optimization algorithms to conduct the parameter estimation experiments on the fractional-order Lorenz chaotic system (Lorenz-FOCS) and the fractional-order Financial chaotic system (Financial-FOCS). In addition, we explored the impact of searching space on parameters estimation through experiments. The experimental results confirmed the feasibility of the modified Salp Swarm Algorithm (MSSA). The MSSA performed better than the SSA and other classical optimization algorithms in terms of the estimation accuracy and convergence rate.

Dynamical Analysis and Adaptive Finite-Time Sliding Mode Control Approach of the Financial Fractional-Order Chaotic System

In this work, we studied the complex behaviors of the fractional-order financial chaotic system, consisting of a simple, relatively chaotic system with two quadratic nonlinearities (QN) and a sextic nonlinearity (SN). We completed and enriched the results presented in the study of Subartini et al. (2021). As a result of this, our study focused more on the fractional order and adaptive finite-time sliding mode control in the financial risk chaotic system. The dynamical behaviors of the financial chaotic system (FCS) with two QN and an SN were analyzed, and the stability was investigated via the Cardano method. The stability analysis showed that the real part of all the roots was negative, which confirmed the stability of the new system under the typical parameters. By using the MATLAB simulation, these properties were characterized, including the phase portraits, 0-1 test, Poincaré map, bifurcation diagram, and Lyapunov exponent. The analysis showed that the financial risk chaotic system of fractional order was able to exhibit chaotic behavior and periodical behavior. In spite of external perturbations and uncertainty, an adaptive finite-time sliding mode control strategy was devised to guide the states of the financial chaotic system to the origin in a finite amount of time. MATLAB phase plots were employed in this study to illustrate all the main results.

Fractional-Order Financial System and Fixed-Time Synchronization

This study is concerned with the dynamic investigation and fixed-time synchronization of a fractional-order financial system with the Caputo derivative. The rich dynamic behaviors of the fractional-order financial system with variations of fractional orders and parameters are discussed analytically and numerically. Through using phase portraits, bifurcation diagrams, maximum Lyapunov exponent diagrams, 0–1 testing and time series, it is found that chaos exists in the proposed fractional-order financial system. Additionally, a complexity analysis is carried out utilizing approximation entropy SE and C0 complexity to detect whether chaos exists. Furthermore, a synchronization controller and an adaptive parameter update law are designed to synchronize two fractional-order chaotic financial systems and identify the unknown parameters in fixed time simultaneously. The estimate of the setting time of synchronization depends on the parameters of the designed controller and adaptive parameter update law, rather than on the initial conditions. Numerical simulations show the effectiveness of the theoretical results obtained.

Analysis of fractal fractional Lorenz type and financial chaotic systems with exponential decay kernels

<abstract><p>In this work, we formulate a fractal fractional chaotic system with cubic and quadratic nonlinearities. A fractal fractional chaotic Lorenz type and financial systems are studied using the Caputo Fabrizo (CF) fractal fractional derivative. This study focuses on the characterization of the chaotic nature, and the effects of the fractal fractional-order derivative in the CF sense on the evolution and behavior of each proposed systems. The stability of the equilibrium points for the both systems are investigated using the Routh-Hurwitz criterion. The numerical scheme, which includes the discretization of the CF fractal-fractional derivative, is used to depict the phase portraits of the fractal fractional chaotic Lorenz system and the fractal fractional-order financial system. The simulation results presented in both cases include the two- and three-dimensional phase portraits to evaluate the applications of the proposed operators.</p></abstract>

Corrigendum to “Chaos in a Financial System with Fractional Order and Its Control via Sliding Mode”

In this paper, the dynamical behaviors and chaos control of a fractional-order financial system are discussed. The lowest fractional order found from which the system generates chaos is 2.49 for the commensurate order case and 2.57 for the incommensurate order case. Also, the period-doubling route to chaos was found in this system. The results of this study were validated by the existence of a positive Lyapunov exponent. Besides, in order to control chaos in this fractional-order financial system with uncertain dynamics, a sliding mode controller is derived. The proposed controller stabilizes the commensurate and incommensurate fractional-order systems. Numerical simulations are carried out to verify the analytical results.

TRACKING CONTROL AND STABILIZATION OF A FRACTIONAL FINANCIAL RISK SYSTEM USING NOVEL ACTIVE FINITE-TIME FAULT-TOLERANT CONTROLS

This paper introduces a fractional-order financial risk system for the first time. Employing well-known tools and analyses such as bifurcations diagrams and spectral entropy, the dynamical behaviors of the system associated with fractional derivative are investigated. The impacts of the fractional derivative on the system’s behavior and its dynamical feature are shown. Then, tracking control and stabilization of the systems are studied. As it is obvious, the existence of faults and failures in the process of control of financial and economic systems is undeniable — this issue necessitates applying proper control techniques for the systems. So as to achieve appropriate results in the control of fractional financial risk system, two finite-time fault-tolerant controllers are proposed, namely, finite-time active fault-tolerant control and finite-time passive fault-tolerant control. Not only do these techniques force the system to reach desired values in finite time, but also, the proposed techniques are robust against uncertainties, faults, and failures in actuators. Through finite-time observers, the effects of all uncertainties are taken to account in the active controller. Finally, numerical simulations of tracking control and stabilization are presented. For numerical simulations, the fractional financial risk system is considered to be in the presence of unknown disturbance as well as faults and failures in actuators. It is assumed that the system is in the presence of various types of actuator faults. Numerical results affirm the ability of the offered control techniques for pushing the states of the fractional-order risk system to the desired value in a short period of time.

Chaos in a Financial System with Fractional Order and Its Control via Sliding Mode

In this paper, the dynamical behaviors and chaos control of a fractional-order financial system are discussed. The lowest fractional order found from which the system generates chaos is 2.49 for the commensurate order case and 2.13 for the incommensurate order case. Also, period-doubling route to chaos was found in this system. The results of this study were validated by the existence of a positive Lyapunov exponent. Besides, in order to control chaos in this fractional-order financial system with uncertain dynamics, a sliding mode controller is derived. The proposed controller stabilizes the commensurate and incommensurate fractional-order systems. Numerical simulations are carried out to verify the analytical results.

Sensitivity analytic and synchronization of a new fractional-order financial system

In this paper, we present a new fractional-order financial system (FOFS) with the new parameters. We study the synchronization for commensurate order of the fractional-order financial system with disturbance observer (FOFSDO) on the new parameters. Also, the sensitivity analysis of the synchronization error was investigated by using the feedback control technique for the FOFSDO. The stability of the used method demonstrates by Lyapunov stability theorem. Numerical simulations are presented to ensure the validity and influence of the target feedback control design in the presence of extrinsic bounded unknown disturbance.

Deep recurrent neural networks with finite-time terminal sliding mode control for a chaotic fractional-order financial system with market confidence

• We develop a new finite-time method for controlling and synchronising fractional-order financial systems • A Deep Learning Recurrent Neural Network is integrated by terminal sliding mode control • The proposed technique is applied to a chaotic fractional-order financial model with market confidence under highly complex and time-varying uncertainties • It is shown comparatively that when the disturbances are high, the conventional TSMC methods fail Disturbances are inevitably found in almost every system and, if not rejected, they could jeopardize the effectiveness of control methods. Thereby, employing state-of-the-art observers could improve the reliability and performance of controllers dramatically. Motivated by this, we develop a new finite-time method for controlling and synchronising fractional-order systems. The deep learning recurrent neural network, which is a strong tool in handling highly complex and time-varying uncertainties, is integrated by terminal sliding mode control. On the basis of Lyapunov stability theorem, the finite-time convergence and stability of the closed-loop system are proven. Then, the proposed technique is applied to a chaotic fractional-order financial model with market confidence. In simulations, it is supposed that the system is operating in the presence of complex disturbances. The results of the proposed technique are compared with terminal sliding mode control. Numerical results illustratively confirm the theoretical claims about the robust performance of the deep learning control technique. Also, it is shown that when the disturbances are high, the conventional methods fail.

Dynamical analysis and adaptive fuzzy control for the fractional-order financial risk chaotic system

In this paper, a fractional-order model of a financial risk dynamical system is proposed and the complex behavior of such a system is presented. The basic dynamical behavior of this financial risk dynamic system, such as chaotic attractor, Lyapunov exponents, and bifurcation analysis, is investigated. We find that numerical results display periodic behavior and chaotic behavior of the system. The results of theoretical models and numerical simulation are helpful for better understanding of other similar nonlinear financial risk dynamic systems. Furthermore, the adaptive fuzzy control for the fractional-order financial risk chaotic system is investigated on the fractional Lyapunov stability criterion. Finally, numerical simulation is given to confirm the effectiveness of the proposed method.

Dynamical behaviour of fractional-order finance system

In this paper, we developed the fractional-order finance system transmission model. The main objective of this paper is to construct and evaluate a fractional derivative to track the shape of the dynamic chaotic financial system of fractional order. The numerical solution for fractional-order financial system is determined using the Atangana–Baleanu–Caputo (ABC) and Caputo derivatives. Picard–Lindelof’s method shows the existence and uniqueness of the solution. Numerical techniques show that ABC derivative strategy can be used effectively to overcome the risk of investment. An active control strategy for controlling chaos is used in this system. The stabilisation of equilibrium is obtained by both theoretical analysis and simulation results.

Chaotic motion of time-delay fractional order financial dynamic system of single sliding mode control

In this paper, the equations of time-delay fractional order financial dynamic system are studied. Firstly, the dynamic characteristics of the time-delay fractional order financial system with are analyzed. Secondly, the attractor state change of the time-delay fractional order financial system induced by disturbance is studied. By adding specific uncertainties and disturbances to the specified state variables of the system, the attractor of the system is induced to change. Finally, the classical integral sliding mode control method is used to design a single controller to control the time-delay fractional order financial system from chaos to fixed point, and the feasibility and robustness of the control scheme have been proved theoretically and verified by the simulation.

Stability control of a novel multidimensional fractional-order financial system with time‐delay via impulse control

Abstract This paper is concerned about the impulsive control of a class of novel nonlinear fractional-order financial system with time-delay. Considering the variation of every states in the fractional-order financial system in the real world has certain delay for various reasons, thus we add corresponding delay on every state variable. Different from the traditional method of stability judgment, we choose two dimensions of time and space to analyze, which makes the process more accurate. In addition, the sufficient condition of the stability criterion for the fractional-order financial system based on impulsive control is derived. Moreover, the impulsive control can not only make the fractional-order financial system stable in different time delay but also in the different fractional operator. Consequently, the impulsive control has generality, universality and strong applicability. In the end, some numerical simulation examples are provided to verify the effectiveness and the benefit of the proposed method.

Fault estimation observer design for fractional-order financial system subject to time-delay

This article is devoted to resolving the issue of time-varying fault in the fractional-order financial system subject to constant delay. A fault-tolerant control is addressed to solve the faults which occur by unpredictable affairs in the financial system. Further, a set of adequate constraints in the form of Linear Matrix Inequalities are derived to ensure asymptotic stability of the considered system under the theorem of Lyapunov. More precisely, Lyapunov–Krasovskii candidates are employed independently with various state-space trajectories such as interest rate, investment demand and price index. Lastly, a numerical example is provided to support the constructed theoretical results.