Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651

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Publisher: search.filters.publisher.Springer London LtdSubject: search.filters.subject.Artificial Neural Networks

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  • Article
    Citation - WoS: 37
    Citation - Scopus: 36
    Fmnsics: Fractional Meyer Neuro-Swarm Intelligent Computing Solver for Nonlinear Fractional Lane-Emden Systems
    (Springer London Ltd, 2022) Raja, Muhammad Asif Zahoor; Umar, Muhammad; Shoaib, Muhammad; Baleanu, Dumitru; Sabir, Zulqurnain
    The fractional neuro-evolution-based intelligent computing has substantial potential to solve fractional order systems represented with Lane-Emden equation arising in astrophysics including Newtonian self-gravitating, spherically symmetric and polytropic fluid. The present study aimed to present a neuro-swarm-based intelligent computing solver for the solution of nonlinear fractional Lane-Emden system (NFLES) using by exploitation of fractional Meyer wavelet artificial neural networks (FMW-ANNs) and global optimization mechanism of particle swarm optimization (PSO) combined with rapid local search of sequential quadratic programming (SQP), i.e., FMW-ANN-PSO-SQP. The motivation for the design of FMW-ANN-PSO-SQP intelligent computing comes with an objective of presenting an accurate, reliable, and viable framworks to deal with stiff nonlinear singular models represented with NFLES involving both fractional and integer derivative terms. The designed algorithm is tested for six different variants of NFLESs. The obtained numerical outcomes obtained by the proposed FMW-ANN-PSO-SQP are compared with the exact results to authenticate the correctness, efficacy, and viability, and these aspects are further endorsed statistical observations.
  • Article
    Citation - WoS: 62
    Citation - Scopus: 53
    Design of Stochastic Numerical Solver for the Solution of Singular Three-Point Second-Order Boundary Value Problems
    (Springer London Ltd, 2021) Baleanu, Dumitru; Shoaib, Muhammad; Raja, Muhammad Asif Zahoor; Sabir, Zulqurnain
    In this paper, a novel meta-heuristic computing solver is presented for solving the singular three-point second-order boundary value problems using artificial neural networks (ANNs) optimized by the combined strength of global and local search ability of genetic algorithms (GAs) and interior point algorithm (IPA), i.e., ANN-GA-IPA. The inspiration for presenting this numerical work comes from the intention of introducing a consistent framework that combines the effective features of neural networks optimized with the contexts of soft computing to handle with such challenging systems. Three numerical variants of singular second-order system have been taken to examine the proficiency, robustness, and stability of the designed approach. The comparison of the proposed results of ANN-GA-IPA from available exact solutions shows the good agreement with 5 to 7 decimal places of the accuracy which established worth of the methodology through performance analyses on a single and multiple executions.