Intelligent Networks for Chaotic Fractional-Order Nonlinear Financial Model
Source abstract
Abstract
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 system. The method is constructed with the combination of the ANNs along with the Levenberg-Marquardt backpropagation (LMB), named the ANNs-LMB. This 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 solution. Fifteen numbers neurons have been used to solve the fractional-order chaotic financial model. The selection of the data to solve the fractional-order chaotic financial model are selected as 75% for training, 10% for testing, and 15% for certification. The results indicate that the presented approximate solutions fit exactly with the reference solution and the method is effective and precise. The 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).
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Chaos control and synchronization · Stock Market Forecasting Methods · Fractional Differential Equations Solutions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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