Open Access

ARTICLE

An Intelligence Computational Approach for the Fractional 4D Chaotic Financial Model

Wajaree Weera¹, Thongchai Botmart^1,*, Charuwat Chantawat¹, Zulqurnain Sabir^2,3, Waleed Adel^4,5, Muhammad Asif Zahoor Raja⁶, Muhammad Kristiawan⁷

1 Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand
2 Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
3 Department of Mathematical Sciences, United Arab Emirates University, P.O. Box 15551, Al Ain, UAE
4 Department of Mathematics and Engineering Physics, Faculty of Engineering, Mansoura University, Egypt
5 Université Française D’Egypte, Ismailia Desert Road, El-Shorouk, Cairo, Egypt
6 Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
7 Science of Education, Universitas Bengkulu, Bengkulu City, Indonesia

* Corresponding Author: Thongchai Botmart. Email:

Computers, Materials & Continua 2023, 74(2), 2711-2724. https://doi.org/10.32604/cmc.2023.033233

Received 11 June 2022; Accepted 01 August 2022; Issue published 31 October 2022

Abstract

The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic procedure. The stochastic procedures mainly depend on the combination of the artificial neural network (ANNs) along with the Levenberg-Marquardt Backpropagation (LMB) i.e., ANNs-LMB technique. The fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional order α. The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between 0 and 1. The data proportion is applied as 73%, 15%, and 12% for training, testing, and certification to solve the chaotic fractional system. The acquired results are verified through the comparison of the reference solution, which indicates the proposed technique is efficient and robust. The 4-D chaotic model is numerically solved by using the ANNs-LMB technique to reduce the mean square error (MSE). To authenticate the exactness, and consistency of the technique, the obtained performances are plotted in the figures of correlation measures, error histograms, and regressions. From these figures, it can be witnessed that the provided technique is effective for solving such models to give some new insight into the physical behavior of the model.

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Keywords

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