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Novel Adaptive Bayesian Regularization Networks for Peristaltic Motion of a Third-Grade Fluid in a Planar Channel

Author

Listed:
  • Tariq Mahmood

    (Department of Mathematics and Statistics, International Islamic University, Islamabad 44000, Pakistan)

  • Nasir Ali

    (Department of Mathematics and Statistics, International Islamic University, Islamabad 44000, Pakistan)

  • Naveed Ishtiaq Chaudhary

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Khalid Mehmood Cheema

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Ahmad H. Milyani

    (Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Muhammad Asif Zahoor Raja

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

Abstract

In this presented communication, a novel design of intelligent Bayesian regularization backpropagation networks (IBRBNs) based on stochastic numerical computing is presented. The dynamics of peristaltic motion of a third-grade fluid in a planar channel is examined by IBRBNs using multilayer structure modeling competency of neural networks trained with efficient optimization ability of Bayesian regularization method. The reference dataset used as inputs and targets parameters of IBRBN has been obtained via the state-of-the-art Adams numerical method. The data of solution dynamics is created for multiple scenarios of the peristaltic transport model by varying the volume flow rate, material parametric of a third-grade fluid model, wave amplitude, and inclination angles. The designed integrated IBRBNs are constructed by exploiting training, testing, and validation operations at each epoch via optimization of a figure of merit on mean square error sense. Exhaustive simulation of IBRBNs with comparison on mean square error, histograms, and regression index substantiated the precision, stability, and reliability to solve the peristaltic transport model.

Suggested Citation

  • Tariq Mahmood & Nasir Ali & Naveed Ishtiaq Chaudhary & Khalid Mehmood Cheema & Ahmad H. Milyani & Muhammad Asif Zahoor Raja, 2022. "Novel Adaptive Bayesian Regularization Networks for Peristaltic Motion of a Third-Grade Fluid in a Planar Channel," Mathematics, MDPI, vol. 10(3), pages 1-23, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:358-:d:732873
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    Citations

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    Cited by:

    1. Ali Najem Alkawaz & Jeevan Kanesan & Anis Salwa Mohd Khairuddin & Irfan Anjum Badruddin & Sarfaraz Kamangar & Mohamed Hussien & Maughal Ahmed Ali Baig & N. Ameer Ahammad, 2023. "Training Multilayer Neural Network Based on Optimal Control Theory for Limited Computational Resources," Mathematics, MDPI, vol. 11(3), pages 1-15, February.
    2. Mukhtar, Roshana & Chang, Chuan-Yu & Raja, Muhammad Asif Zahoor & Chaudhary, Naveed Ishtiaq & Shu, Chi-Min, 2024. "Novel nonlinear fractional order Parkinson's disease model for brain electrical activity rhythms: Intelligent adaptive Bayesian networks," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).

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