Prediction and Analysis of ChiNext Stock Price Based on Linear and Non-linear Composite Model

Jiang, Yueting; Abdeldayem, Marwan Mohamed

doi:10.2478/amns.2022.2.0055

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Eventually, modern IPTs have been instilled virtually in all felds for continuous optimization and have forced excellent improvements into the earlier procedures. In [69][70][71][72], the authors have presented the applications of higher-order diferential equations in fnancial and business predictions/forecasts based on the nonlinear and linear types of models. Figure 2 represents the complete structure of the hybrid solver for the singularly perturbed delay model.…”

Section: Learning Techniquesmentioning

confidence: 99%

Integrated Stochastic Investigation of Singularly Perturbed Delay Differential Equations for the Neuronal Variability Model

Ahmad,

Hussain,

Ilyas

et al. 2023

International Journal of Intelligent Systems

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The proposed research utilizes a computational approach to attain a numerical solution for the singularly perturbed delay differential equation (SPDDE) problem arising in the neuronal variability model through artificial neural networks (ANNs) with different solvers. The log-sigmoid function is used to construct the fitness function. The implementation of ANN on SPDDE problems is formulated for different solvers and trained with different weights. The optimization solvers such as the genetic algorithm (GA), sequential quadratic programming (SQP), and pattern search (PS) are hybridized with the active set technique (AST) and the interior-point technique (IPT) and is used to check the accuracy and rapid convergence of the numerical results of the SPDDE model. The numerical outcomes demonstrate that the system is easy to handle and efficient to solve with boundary conditions. Moreover, we used the mean residual error for one hundred runs for each solver to validate the accuracy of the proposed scheme.

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Section: Learning Techniquesmentioning

confidence: 99%