Scopus İndeksli Yayınlar Koleksiyonu

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

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Author: search.filters.author.Sabir, ZulqurnainPublisher: search.filters.publisher.Springer Heidelberg

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Now showing 1 - 4 of 4
  • Article
    Citation - WoS: 12
    Citation - Scopus: 17
    Dynamics of Multi-Point Singular Fifth-Order Lane-Emden System With Neuro-Evolution Heuristics
    (Springer Heidelberg, 2022) Ali, Mohamed R.; Fathurrochman, Irwan; Raja, Muhammad Asif Zahoor; Sadat, R.; Baleanu, Dumitru; Sabir, Zulqurnain
    The objective of the presented communication is to examine and analyze the solutions of nonlinear multi-singular fifth-order Lane-Emden (LE) system for different scenarios by variation of shape factors settled on the equivalent design of the LE equations. The neuro-evolution based stochastic computing is explored for the numerical measures using the artificial neural networks (ANNs) models for the appropriate continuous mapping, while the learning of decision variables is conducted using the integrated meta-heuristic global search of genetic algorithms (GA) hybrid with the local search efficiency of active-set (AS) i.e., ANN-GA-AS scheme. The numerical approach ANN-GA-AS is applied efficiently for the fifth kind of nonlinear LE model and statistical calculations further validate the accuracy, robustness as well as convergence.
  • Article
    Citation - WoS: 21
    Citation - Scopus: 22
    Design of Neuro-Swarming Computational Solver for the Fractional Bagley-Torvik Mathematical Model
    (Springer Heidelberg, 2022) Sabir, Zulqurnain; Raja, Muhammad Asif Zahoor; Baleanu, Dumitru; Guirao, Juan L. G.
    This study is to introduce a novel design and implementation of a neuro-swarming computational numerical procedure for numerical treatment of the fractional Bagley-Torvik mathematical model (FBTMM). The optimization procedures based on the global search with particle swarm optimization (PSO) and local search via active-set approach (ASA), while Mayer wavelet kernel-based activation function used in neural network (MWNNs) modeling, i.e., MWNN-PSOASA, to solve the FBTMM. The efficiency of the proposed stochastic solver MWNN-GAASA is utilized to solve three different variants based on the fractional order of the FBTMM. For the meticulousness of the stochastic solver MWNN-PSOASA, the obtained and exact solutions are compared for each variant of the FBTMM with reasonable accuracy. For the reliability of the stochastic solver MWNN-PSOASA, the statistical investigations are provided based on the stability, robustness, accuracy and convergence metrics.
  • Article
    Citation - WoS: 44
    Citation - Scopus: 49
    Evolutionary Computing for Nonlinear Singular Boundary Value Problems Using Neural Network, Genetic Algorithm and Active-Set Algorithm
    (Springer Heidelberg, 2021) Khalique, Chaudry Masood; Raja, Muhammad Asif Zahoor; Baleanu, Dumitru; Sabir, Zulqurnain
    In this numerical study, a class of nonlinear singular boundary value problem is solved by implementation of a novel meta-heuristic computing tool based on the artificial neural networks (ANNs) modeling of system and the optimization of decision variable of ANNs through the combined strength of global search via genetic algorithms (GA) and local search ability of active-set algorithm (ASA), i.e., ANN-GA-ASA. The proposed intelligent computing solver ANN-GA-ASA exploits the input, hidden, and output layers' structure of ANNs. This is to represent the differential model in the nonlinear singular second-order periodic boundary value problems, which are connected to form an error-based objective function (OF) and optimize the OF by the integrated heuristics of GA-ASA. The purpose to present this research is to associate the operational legacy of neural networks and to challenge such kinds of inspiring models. Two different examples of the singular periodic model have been investigated to observe the robustness, proficiency and stability of the ANN-GA-ASA. The proposed outcomes of ANN-GA-ASA are compared with reference to true results so as to establish the value of the designed scheme. Exhaustive comparison has been made and presented between the Log-sigmoidal ANNs results and the radial basis ANNs outcomes. The reliability of the results obtained is endorsed by using both types of networks as well as the value of designed schemes.
  • Article
    Citation - WoS: 142
    Citation - Scopus: 149
    A New Stochastic Computing Paradigm for the Dynamics of Nonlinear Singular Heat Conduction Model of the Human Head
    (Springer Heidelberg, 2018) Umar, Muhammad; Sabir, Zulqurnain; Khan, Junaid Ali; Baleanu, Dumitru; Raja, Muhammad Asif Zahoor
    Bio-inspired computing approaches are effective to solve a variety of dynamical problems. The strength of these stochastic solvers is exploited for the numerical treatment of a nonlinear heat conduction model of the human head using artificial neural networks (ANNs), genetic algorithms (GAs), active-set technique (AST), and their hybrids. The universal function approximation competencies of unsupervised ANNs are utilized in constructing the mathematical model of the problem by defining an error function in the mean squared sense. The training of the design parameters of ANN models is made with global search brilliance of GAs, viably local search with AST and hybrid approach GA-AST. The results of the proposed schemes are determined in terms of temperature profiles by considering variants of the problem with different Biot numbers, metabolic thermogenesis slope parameters and thermogenesis heat production factors. The correctness, effectiveness and convergence of the proposed approaches are also ascertained through statistics.