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Intelligent Networks for Chaotic Fractional-Order Nonlinear Financial Model

Computers, Materials & Continua 2022年第9期72卷 5015-5030页

作者： Prem Junswang Zulqurnain Sabir Muhammad Asif Zahoor Raja Waleed Adel Thongchai Botmart Wajaree Weera Department of Statistics Faculty of ScienceKhon Kaen UniversityKhon Kaen 40002Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Electrical Engineering COMSATS Institute of Information TechnologyAttockPakistan Department of Mathematics and Engineering Physics Faculty of EngineeringMansoura UniversityEgypt UniversitéFrançaise D’Egypte Ismailia Desert RoadEl-ShoroukCairoEgypt Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen 40002Thailand

The purpose of this paper is to present a numerical approach based on the artificial neural networks(ANNs)for solving a novel fractional chaotic financial model that represents the effect of memory and chaos in the presented *** method is constructed with the combination of the ANNs along with the Levenberg-Marquardt backpropagation(LMB),named the *** technique is tested for solving the novel problem for three cases of the fractional-order values and the obtained results are compared with the reference *** numbers neurons have been used to solve the fractional-order chaotic financial *** selection of the data to solve the fractional-order chaotic financial model are selected as 75%for training,10%for testing,and 15%for *** results indicate that the presented approximate solutions fit exactly with the reference solution and the method is effective and *** obtained results are testified to reduce the mean square error(MSE)for solving the fractional model and verified through the various measures including correlation,MSE,regression histogram of the errors,and state transition(ST).

关键词： Financial model chaotic fractional-order reference dataset artificial neural networks levenberg-marquardt backpropagation

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Nonlinear Dynamics of Nervous Stomach Model Using Supervised Neural Networks

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Computers, Materials & Continua 2022年第7期72卷 1627-1644页

作者： Zulqurnain Sabir Manoj Gupta Muhammad Asif Zahoor Raja N.Seshagiri Rao Muhammad Mubashar Hussain Faisal Alanazi Orawit Thinnukool Pattaraporn Khuwuthyakorn Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Electronics and Communication Engineering JECRC UniversityJaipur(Rajasthan)303905India Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan Department of Applied Mathematics School of Applied Natural SciencesAdama Science and Technology UniversityAdamaEthiopia Department of Mathematics University of PunjabJhelum CampusPakistan Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityCollege of EngineeringAl Kharj16278Saudi Arabia Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

The purpose of the current investigations is to solve the nonlinear dynamics based on the nervous stomach model(NSM)using the supervised neural networks(SNNs)along with the novel features of Levenberg-Marquardt backpropagation technique(LMBT),i.e.,*** SNNs-LMBT is implemented with three different types of sample data,authentication,testing and *** ratios for these statistics to solve three different variants of the nonlinear dynamics of the NSM are designated 75%for training,15%for validation and 10%for testing,*** the numerical measures of the nonlinear dynamics of the NSM,the Runge-Kutta scheme is implemented to form the reference *** attained numerical form of the nonlinear dynamics of the NSM through the SNNs-LMBT is implemented in the reduction of the mean square error(MSE).For the exactness,competence,reliability and efficiency of the proposed SNNs-LMBT,the numerical actions are capable using the proportional arrangements through the features of the MSE results,error histograms(EHs),regression and correlation.

关键词： Nonlinear dynamics nervous stomach system reference dataset levenberg-marquardt backpropagation technique numerical outcomes

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

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Computers, Materials & Continua 2023年第2期74卷 2711-2724页

作者： Wajaree Weera Thongchai Botmart Charuwat Chantawat Zulqurnain Sabir Waleed Adel Muhammad Asif Zahoor Raja Muhammad Kristiawan Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Mathematical Sciences United Arab Emirates UniversityP.O.Box 15551Al AinUAE Department of Mathematics and Engineering Physics Faculty of EngineeringMansoura UniversityEgypt UniversitéFrançaise D’Egypte Ismailia Desert RoadEl-ShoroukCairoEgypt Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin 64002Taiwan Science of Education Universitas BengkuluBengkulu CityIndonesia

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 *** stochastic procedures mainly depend on the combination of the artificial neural network(ANNs)along with the Levenberg-Marquardt Backpropagation(LMB)i.e.,ANNs-LMB *** fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional orderα.The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between 0 and *** data proportion is applied as 73%,15%,and 12%for training,testing,and certification to solve the chaotic fractional *** acquired results are verified through the comparison of the reference solution,which indicates the proposed technique is efficient and *** 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 *** 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.

关键词： Financial model chaotic Levenberg-Marquardt Backpropagation fractional order artificial neural networks reference dataset

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