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Swarming Computational Efficiency to Solve a Novel Third-Order Delay Differential Emden-Fowler System

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Computers, Materials & Continua 2022年第12期73卷 4833-4849页

作者： Wajaree Weera Zulqurnain Sabir Muhammad Asif Zahoor RAJA Sakda Noinang Thongchai Botmart Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin64002Taiwan Department of Mathematics Statistics and Computer Faculty of ScienceUbon Ratchathani UniversityUbon Ratchathani34190Thailand

The purpose of this research is to construct an integrated neuro swarming scheme using the procedures of the artificial neural networks(ANNs)with the use of global search particle swarm optimization(PSO)along with the competent local search interior-point programming(IPP)called as ANN-PSOIPP.The proposed computational scheme is implemented for the numerical simulations of the third order nonlinear delay differential Emden-Fowler model(TON-DD-EFM).The TON-DD-EFM is based on two types along with the particulars of shape factor,delayed terms,and singular points.A merit function is performed using the optimization of PSOIPP to find the solutions to the TON-DD-EFM.The effectiveness of the ANN-PSOIPP is certified through the comparison with the exact results for solving four examples of the TON-DD-EFM.The scheme’s efficiency is observed by performing the absolute error in suitable measures found around 10−04 to 10−07.Furthermore,the statistical-based assessments for 100 trials are provided to compute the accuracy,stability,and constancy of the ANNPSOIPP for solving the TON-DD-EFM.

关键词： Third-order nonlinear emden-fowler system artificial neural network statistical results particle swarm optimization numerical experimentations local search programming

A Stochastic Study of the Fractional Order Model of Waste Plastic in Oceans

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Computers, Materials & Continua 2022年第11期73卷 4441-4454页

作者： Muneerah Al Nuwairan Zulqurnain Sabir Muhammad Asif Zahoor RAJA Maryam Alnami Hanan Almuslem Department of Mathematics and Statistics College of ScienceKing Faisal UniversityP.O.Box 400Al-Ahsa31982Saudi Arabia Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliouYunlin64002Taiwan

In this paper,a fractional order model based on the management of waste plastic in the ocean(FO-MWPO)is numerically investigated.The mathematical form of the FO-MWPO model is categorized into three components,waste plastic,Marine debris,and recycling.The stochastic numerical solvers using the Levenberg-Marquardt backpropagation neural networks(LMQBP-NNs)have been applied to present the numerical solutions of the FO-MWPO system.The competency of the method is tested by taking three variants of the FO-MWPO model based on the fractional order derivatives.The data ratio is provided for training,testing and authorization is 77%,12%,and 11%respectively.The exactness of LMQBP-NNs is observed by using the comparative performances of the obtained and the Adams-BashforthMoulton method.To verify the competence,validity,capability,exactness,and consistency of LMQBP-NNs,the performances have been obtained using the regression,state transitions,error histograms,correlation and mean square error.

关键词： Fractional order ocean Adams-Bashforth-Moulton LevenbergMarquardt backpropagation numerical solutions

Intelligent computing networks for nonlinear influenza-A epidemic model

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International Journal of Biomathematics 2023年第4期16卷 125-154页

作者： Nabeela Anwar muhammad Shoaib Iftikhar Ahmad Shafaq Naz Adiqa Kausar Kiani Muhammad Asif Zahoor RAJA Department of Mathematics University of Gujrat Punjab 50700 Pakistan Department of Mathematics COMSATS University Islamabad Attock Campus Attock 43600 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 UniversityRoad Section 3 DouliouYunlin 64002TaiwanR.O.C.

The differential equations having delays take paramount interest in the research community due to their fundamental role to interpret and analyze the mathematical models arising in biological studies.This study deals with the exploitation of knack of artificial intelligence-based computing paradigm for numerical treatment of the functional delay differential systems that portray the dynamics of the nonlinear influenza-A epidemic model(IA-EM)by implementation of neural network backpropagation with Levenberg-Marquardt scheme(NNBLMS).The nonlinear IA-EM represented four classes of the population dynamics including susceptible,exposed,infectious and recovered individuals.The referenced datasets for NNBLMS are assembled by employing the Adams method for sufficient large number of scenarios of nonlinear IA-EM through the variation in the infection,turnover,disease associated death and recovery rates.The arbitrary selection of training,testing as well as validation samples of dataset are utilizing by designed NNBLMS to calculate the approximate numerical solutions of the nonlinear IA-EM develop a good agreement with the reference results.The proficiency,reliability and accuracy of the designed NNBLMS are further substantiated via exhaustive simulations-based outcomes in terms of mean square error,regression index and error histogram studies.

关键词： Functional delay differential systems nonlinear influenza-A epidemic model Adams method neural networks Levenberg-Marquardt scheme intelligent computing regression analysis

Bio-inspired computational heuristics to study models of HIV infection of CD4＋ T-cell

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International Journal of Biomathematics 2018年第2期11卷 83-114页

作者： Muhammad Asif Zahoor RAJA Kiran Asma muhammad Saeed Aslam Department of Electrical Engineering COMSATS Institute of Information Technology Attoek Campus Attock Pakistan Department of Computer Sciences COMSATS Institute of Information Technology Attock Campus Attock Pakistan Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad Pakistan

In this work, biologically-inspired computing framework is developed for HIV infection of CD4 T-cell model using feed-forward artificial neural networks (ANNs), genetic algorithms (GAs), sequential quadratic programming (SQP) and hybrid approach based on GA-SQP. The mathematical model for HIV infection of CD4 T-cells is represented with the help of initial value problems (IVPs) based on the system of ordinary differential equations (ODEs). The ANN model for the system is constructed by exploiting its strength of universal approximation. An objective function is developed for the system through unsupervised error using ANNs in the mean square sense. Training with weights of ANNs is carried out with GAs for effective global search supported with SQP for efficient local search. The proposed scheme is evaluated on a number of scenarios for the HIV infection model by taking the different levels for infected cells, natural substitution rates of uninfected cells, and virus particles. Comparisons of the approximate solutions are made with results of Adams numerical solver to establish the correctness of the proposed scheme. Accuracy and convergence of the approach are validated through the results of statistical analysis based on the sufficient large number of independent runs. [ABSTRACT FROM AUTHOR]

关键词：计算框架 HIV 房间感染启发规则模特儿简历学习

Novel Computing for the Delay Differential Two-Prey and One-Predator System

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Computers, Materials & Continua 2022年第10期73卷 249-263页

作者： Prem Junsawang Zulqurnain Sabir Muhammad Asif Zahoor RAJA Soheil Salahshour Thongchai Botmart Wajaree Weera Department of Statistics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliou64002Taiwan Faculty of Engineering and Natural Sciences Bahcesehir UniversityIstanbulTurkey Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

The aim of these investigations is to find the numerical performances of the delay differential two-prey and one-predator system.The delay differential models are very significant and always difficult to solve the dynamical kind of ecological nonlinear two-prey and one-predator system.Therefore,a stochastic numerical paradigm based artificial neural network(ANN)along with the Levenberg-Marquardt backpropagation(L-MB)neural networks(NNs),i.e.,L-MBNNs is proposed to solve the dynamical twoprey and one-predator model.Three different cases based on the dynamical two-prey and one-predator system have been discussed to check the correctness of the L-MBNNs.The statistic measures of these outcomes of the dynamical two-prey and one-predator model are chosen as 13%for testing,12%for authorization and 75%for training.The exactness of the proposed results of L-MBNNs approach for solving the dynamical two-prey and onepredator model is observed with the comparison of the Runge-Kutta method with absolute error ranges between 10−05 to 10−07.To check the validation,constancy,validity,exactness,competence of the L-MBNNs,the obtained state transitions(STs),regression actions,correlation presentations,MSE and error histograms(EHs)are also provided.

关键词： Delay differential model dynamical system prey-predator Levenberg-Marquardt backpropagation MSE neural networks

An Advanced Stochastic Numerical Approach for Host-Vector-Predator Nonlinear Model

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Computers, Materials & Continua 2022年第9期72卷 5823-5843页

作者： Prem Junswang Zulqurnain Sabir Muhammad Asif Zahoor RAJA Soheil Salahshour Thongchai Botmart Wajaree Weera Department of Statistics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliouYunlin64002Taiwan Faculty of Engineering and Natural Sciences Bahcesehir UniversityIstanbulTurkey Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

A novel design of the computational intelligent framework is presented to solve a class of host-vector-predator nonlinear model governed with set of ordinary differential equations.The host-vector-predator nonlinear model depends upon five groups or classes,host plant susceptible and infected populations,vectors population of susceptible and infected individuals and the predator population.An unsupervised artificial neural network is designed using the computational framework of local and global search competencies of interior-point algorithm and genetic algorithms.For solving the hostvector-predator nonlinear model,a merit function is constructed using the differential model and its associated boundary conditions.The optimization of this merit function is performed using the computational strength of designed integrated heuristics based on interior point method and genetic algorithms.For the comparison,the obtained numerical solutions of networks models optimized with efficacy of global search of genetic algorithm and local search with interior point method have been compared with the Adams numerical solver based results or outcomes.Moreover,the statistical analysis will be performed to check the reliability,robustness,viability,correctness and competency of the designed integrated heuristics of unsupervised networks trained with genetic algorithm aid with interior point algorithm for solving the biological based host-vector-predator nonlinear model for sundry scenarios of paramount interest.

关键词： Nonlinear host-vector-predator system adams results global/local search methods optimization neural networks statistical analysis

无次要路径主动噪声控制系统的生物启发式与内点混合法（英文）

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Frontiers of Information Technology & Electronic Engineering 2018年第2期19卷 246-259页

作者： Muhammad Asif Zahoor RAJA muhammad Saeed ASLAM Naveed Ishtiaq CHAUDHARY Wasim Ullah KHAN Department of Electrical Engineering COMSATS Institute of Information TechnologyAttock Campus Pakistan Institute of Engineering and Applied Sciences Department of Electronic Engineering International Islamic University

开发了一种主动噪声控制(active noise control,ANC)系统的混合计算框架,运用基于遗传算法(geneticalgorithm,GA)和内点法(interior-point method,IPM)的进化计算技术,集成得到GA-IPM方法。标准ANC系统通常采用滤波扩展最小均方算法优... 详细信息

开发了一种主动噪声控制(active noise control,ANC)系统的混合计算框架,运用基于遗传算法(geneticalgorithm,GA)和内点法(interior-point method,IPM)的进化计算技术,集成得到GA-IPM方法。标准ANC系统通常采用滤波扩展最小均方算法优化线性有限脉冲响应滤波器的系数,但易陷入局部极小值(local minima,LM)。本文提出的GA-IPM计算方法有效解决了上述问题。该法不易出现LM问题,且无需识别方案中ANC系统的次级路径。采用正弦、随机和复杂随机噪声干扰下的耳机ANC模型,对该方法在几种线性和非线性主级和次级路径状况下的表现进行评估。大量独立运行算法的统计分析结果验证了该方案的准确性和收敛性。

关键词：控制系统噪音控制启发规则路径混合简历鉴定基因算法

Unsupervised neural networks for solving Troesch＇s problem

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Chinese Physics B 2014年第1期23卷 546-556页

作者： raja, muhammad asif zahoor , rasifzahoor@*** Department of Electrical Engineering COMSATS Institute of Information Technology Attock Campus Attock Pakistan

In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm (GA) and pattern search (PS) techniques are used as the global search methods and the interior point method (IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM (GA-IPM) and PS hybridized with IPM (PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs. [ABSTRACT FROM AUTHOR]

关键词： GENETIC algorithms COMBINATORIAL optimization EVOLUTIONARY algorithms EVOLUTIONARY computation PHYSICS

Differential evolution based computation intelligence solver for elliptic partial differential equations

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Frontiers of Information Technology & Electronic Engineering 2019年第10期20卷 1445-1456页

作者： muhammad Faisal FATEH Aneela ZAMEER Sikander MMIRZA Nasir MMIRZA muhammad Saeed ASLAM Muhammad Asif Zahoor RAJA Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences(PIEAS)Islamabad 45650Pakistan Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences(PIEAS)Islamabad 45650Pakistan Department of Physics and Applied Mathematics Pakistan Institute of Engineering and Applied Sciences(PIEAS)Islamabad 45650Pakistan School of Electrical and Electronic Engineering University of AdelaideAdelaide 5005Australia Department of Electrical and Computer Engineering COMSATS University IslamabadAttock CampusAttock 43600Pakistan

A differential evolution based methodology is introduced for the solution of elliptic partial differential equations (PDEs) with Dirichlet and/or Neumann boundary conditions. The solutions evolve over bounded domains throughout the interior nodes by minimization of nodal deviations among the population. The elliptic PDEs are replaced by the corresponding system of finite difference approximation, yielding an expression for nodal residues. The global residue is declared as the root-mean-square value of the nodal residues and taken as the cost function. The standard differential evolution is then used for the solution of elliptic PDEs by conversion to a minimization problem of the global residue. A set of benchmark problems consisting of both linear and nonlinear elliptic PDEs has been considered for validation, proving the effectiveness of the proposed algorithm. To demonstrate its robustness, sensitivity analysis has been carried out for various differential evolution operators and parameters. Comparison of the differential evolution based computed nodal values with the corresponding data obtained using the exact analytical expressions shows the accuracy and convergence of the proposed methodology.

关键词： Differential evolution Boundary value problems Partial differential equation Finite difference scheme Numerical computing