A Bayesian regularization radial basis neural network novel procedure for the fractional economic and environmental system

The motive of current work is to design a novel radial basis Bayesian regularization neural network (RB-BRNN) for solving the nonlinear fractional economic and environmental system (FEES). A radial basis activation function in the hidden layers is applied by taking 20 numbers of neurons. The mathema...

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Bibliographic Details
Published inInternational journal of computer mathematics Vol. 102; no. 2; pp. 280 - 291
Main Authors Chen, Qiliang, Sabir, Zulqurnain, Umar, Muhammad, Mehmet Baskonus, Haci
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 01.02.2025
Taylor & Francis Ltd
Subjects
Bayesian analysis
Bayesian regularization
Dynamical systems
economic/environmental
Errors
Fractional
Functions (mathematics)
Neural networks
Nonlinear dynamics
radial basis
Regularization
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Summary:The motive of current work is to design a novel radial basis Bayesian regularization neural network (RB-BRNN) for solving the nonlinear fractional economic and environmental system (FEES). A radial basis activation function in the hidden layers is applied by taking 20 numbers of neurons. The mathematical FEES is presented in three classes, named as cost of control accomplishment, manufacturing elements competence and technical exclusion's diagnostics cost. A reference dataset is obtained using the Adams numerical results to reduce the mean square error (MSE) by taking the data for training 70%, while 15% is used for both testing and validation. The negligible absolute error values and comparison of the solutions develop the worth of computing RB-BRNN in order to solve the nonlinear dynamics of the FEES. Error diagrams, regression values, and the MSE performances are implemented to assess the precision of the designed solver.
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ISSN:0020-7160
1029-0265
DOI:10.1080/00207160.2024.2409794