检索结果-内蒙古大学图书馆

Gudermannian neural networks using the optimization procedures of genetic algorithm and active set approach for the three-species food chain nonlinear model

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第7期14卷 8913-8922页

作者： Sabir, Zulqurnain Ali, Mohamed R. Sadat, R. Hazara Univ Dept Math & Stat Mansehra Pakistan Future Univ Fac Engn & Technol Cairo Egypt Benha Univ Fac Engn Benha Dept Basic Sci Banha 13512 Egypt Zagazig Univ Fac Engn Dept Phys & Engn Math Zagazig Egypt

The present study is to investigate the Gudermannian neural networks (GNNs) using the optimization procedures of genetic algorithm and active-set approach (GA-ASA) to solve the three-species food chain nonlinear model. The three-species food chain nonlinear model is dependent upon the prey populations, top-predator, and specialist predator. The design of an error-based fitness function is presented using the sense of the three-species food chain nonlinear model and its initial conditions. The numerical results of the model have been obtained by exploiting the GNN-GA-ASA. The obtained results through the GNN-GA-ASA have been compared with the Runge-Kutta method to substantiate the correctness of the designed approach. The reliability, efficacy and authenticity of the proposed GNN-GA-ASA are examined through different statistical measures based on single and multiple neurons for solving the three-species food chain nonlinear model.

关键词： Gudermannian neural network Three-dimensional food chain nonlinear model Nonlinear differential system Runge-Kutta scheme active-set algorithm Statistical studies

来源：评论

学校读者我要写书评

暂无评论

Swarming Computational Approach for the Heartbeat Van Der Pol Nonlinear System

引用

Computers, Materials & Continua 2022年第9期72卷 6185-6202页

作者： Muhammad Umar Fazli Amin Soheil Salahshour Thongchai Botmart Wajaree Weera Prem Junswang Zulqurnain Sabir Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Faculty of Engineering and Natural Sciences Bahcesehir UniversityIstanbulTurkey Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Department of Statistics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

The present study is related to design a stochastic framework for the numerical treatment of the Van der Pol heartbeat model(VP-HBM)using the feedforward artificial neural networks(ANNs)under the optimization of particle swarm optimization(PSO)hybridized with the active-set algorithm(ASA),i.e.,*** global search PSO scheme and local refinement of ASA are used as an optimization procedure in this *** error-based merit function is defined using the differential VP-HBM form as well as the initial *** optimization of the merit function is accomplished using the hybrid computing performances of *** designed performance of ANNs-PSO-ASA is implemented for the numerical treatment of the VP-HBM dynamics by fluctuating the pulse shape adjustment terms,external forcing factor and damping coefficient with fixed ventricular contraction *** perform the correctness of the present scheme,the obtained numerical results through the designed ANN-PSO-ASA will be compared with the Adams numerical *** statistical investigations with larger dataset are provided using the“mean absolute deviation”,“Theil’s inequality coefficient”and“variance account for”operators to perform the applicability,reliability,and effectiveness of the designed ANNs-PSO-ASA scheme for solving the VP-HBM.

关键词： Particle swarm optimization van der Pol heartbeat system statistical analysis artificial neural networks active-set algorithm numerical computing

来源：评论

学校读者我要写书评

暂无评论

Numerical investigations of the nonlinear smoke model using the Gudermannian neural networks

引用

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第1期19卷 351-370页

作者： Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Alnahdi, Abeer S. Jeelani, Mdi Begum Abdelkawy, M. A. Hazara Univ Dept Math & Stat Mansehra Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Imam Mohammad Ibn Saud Islamic Univ Fac Sci Dept Math & Stat Riyadh Saudi Arabia Beni Suef Univ Fac Sci Dept Math Bani Suwayf Egypt

These investigations are to find the numerical solutions of the nonlinear smoke model to exploit a stochastic framework called gudermannian neural works (GNNs) along with the optimization procedures of global/local search terminologies based genetic algorithm (GA) and interior-point algorithm (IPA), i.e., GNNs-GA-IPA. The nonlinear smoke system depends upon four groups, temporary smokers, potential smokers, permanent smokers and smokers. In order to solve the model, the design of fitness function is presented based on the differential system and the initial conditions of the nonlinear smoke system. To check the correctness of the GNNs-GA-IPA, the obtained results are compared with the Runge-Kutta method. The plots of the weight vectors, absolute error and comparison of the results are provided for each group of the nonlinear smoke model. Furthermore, statistical performances are provided using the single and multiple trial to authenticate the stability and reliability of the GNNs-GA-IPA for solving the nonlinear smoke system.

关键词： Gudermannain neural networks nonlinear smoke model Runge-Kutta active-set algorithm genetic algorithms numerical results

来源：评论

学校读者我要写书评

暂无评论

Numerical Solutions of a Novel Designed Prevention Class in the HIV Nonlinear Model

引用

Computer Modeling in Engineering & Sciences 2021年第10期129卷 227-251页

作者： Zulqurnain Sabir Muhammad Umar Muhammad Asif Zahoor Raja Dumitru Baleanu Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan Department of Mathematics Cankaya UniversityAnkara06790Turkey Institute of Space Science MagureleBucharest77125Romania

The presented research aims to design a new prevention class(P)in the HIV nonlinear system,i.e.,the HIPV *** numerical treatment of the newly formulated HIPV model is portrayed handled by using the strength of stochastic procedure based numerical computing schemes exploiting the artificial neural networks(ANNs)modeling legacy together with the optimization competence of the hybrid of global and local search schemes via genetic algorithms(GAs)and active-set approach(ASA),i.e.,*** optimization performances through GA-ASA are accessed by presenting an error-based fitness function designed for all the classes of the HIPV model and its corresponding initial conditions represented with nonlinear systems of *** check the exactness of the proposed stochastic scheme,the comparison of the obtained results and Adams numerical results is *** the convergence measures,the learning curves are presented based on the different contact rate ***,the statistical performances through different operators indicate the stability and reliability of the proposed stochastic scheme to solve the novel designed HIPV model.

关键词： Prevention class HIV supervised neural networks infection model artificial neural networks convergence curves active-set algorithm adams results genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Circulating Current Control for the Modular Multilevel Matrix Converter Based on Model Predictive Control

引用

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2021年第5期9卷 6069-6085页

作者： Urrutia, Matias Cardenas, Roberto Clare, Jon C. Watson, Alan Univ Chile Dept Elect Engn Santiago 8370451 Chile Univ Nottingham PEMC Res Grp Fac Engn Nottingham NG7 2RD England

In this work, a continuous-control-set model predictive control (CCS-MPC) strategy, with a saturation scheme for protection, is presented for regulating the circulating currents of a modular multilevel matrix converter (M3C). The proposed approach is based on a state-space model of the M3C and allows protection and better utilization of the devices through a saturation scheme, which directly limits the arm currents and cluster output voltages by integrating the corresponding bounds as constraints of the CCS-MPC formulation. In order to solve the inherent optimization problem associated with the CCS-MPC, an active-set algorithm is implemented. Experimental and simulation results from a 27-cell M3C prototype validate the proposed strategy and illustrate the good performance achieved with the methodology presented in this work.

关键词： Power electronics Voltage control Frequency control Capacitors Matrix converters Topology Predictive control active-set algorithm circulating currents model predictive control (MPC) modular multilevel matrix converter (M3C)

来源：评论

学校读者我要写书评

暂无评论

Integrated intelligent computing paradigm for nonlinear multi-singular third-order Emden-Fowler equation

引用

NEURAL COMPUTING & APPLICATIONS 2021年第8期33卷 3417-3436页

作者： Sabir, Zulqurnain Umar, Muhammad Guirao, Juan L. G. Shoaib, Muhammad Raja, Muhammad Asif Zahoor Hazara Univ Dept Math & Stat Mansehra Pakistan Tech Univ Cartagena Hosp Marina Dept Appl Math & Stat Cartagena 30203 Spain COMSATS Univ Islamabad Dept Math Attock Campus Attock 43600 Pakistan COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock 43600 Pakistan

In this study, an advance computational intelligence scheme is designed and implemented to solve third-order nonlinear multiple singular systems represented with Emden-Fowler differential equation (EFDE) by exploiting the efficacy of artificial neural networks (ANNs), genetic algorithms (GAs) and active-set algorithm (ASA), i.e., ANN-GA-ASA. In the scheme, ANNs are used to discretize the EFDE for formulation of mean squared error-based fitness function. The optimization task for ANN models of nonlinear multi-singular system is performed by integrated competency GA and ASA. The efficiency of the designed ANN-GA-ASA is examined by solving five different variants of the singular model to check the effectiveness, reliability and significance. The statistical investigations are also performed to authenticate the precision, accuracy and convergence.

关键词： Nonlinear Emden-Fowler equation Artificial neural networks Statistical analysis Genetic algorithms Singular systems active-set algorithm Hybrid computing

来源：评论

学校读者我要写书评

暂无评论

Optimizing the selection and scheduling of multi-class projects using a Stackelberg framework

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第2期286卷 508-522页

作者： Miralinaghi, Mohammad Seilabi, Sania E. Chen, Sikai Hsu, Yu-Ting Labi, Samuel Purdue Univ Lyles Sch Civil Engn 550 Stadium Mall Dr W Lafayette IN 47907 USA Purdue Univ CCAT NEXTRANS Ctr 550 Stadium Mall Dr W Lafayette IN 47907 USA Natl Taiwan Univ Dept Civil Engn Room 305Civil Engn Bldg 1Sec 4Roosevelt Rd Taipei 10617 Taiwan

One of the major causes of non-recurrent traffic congestion in urban areas is the implementation of transport infrastructure projects on city roads. The seeming ubiquity of work zones in cities causes road user frustration and safety hazards, and public relations problems for the transport agency. For this reason, transport agencies seek strategic ways to not only select urban projects but also schedule them in a manner that minimizes the effort associated with these functions. In other words, they seek to exploit the synergies between the tasks of project selection and project scheduling while duly accommodating the project interdependencies. This study introduces a general framework that simultaneously and optimally selects and schedules urban road projects subject to budgetary constraints over a given planning horizon. The project classes considered in this study are lane addition, new road construction, and road maintenance. Through a mimicry of the classic Stackelberg leader-follower game, this problem is formulated herein as a bi-level program. In the upper level, the leader (transport agency decision-makers) determines an optimal set of projects from a larger pool of candidate projects and decides an optimal schedule for their implementation. In the lower level, the followers (road users) seek to minimize their travel delays based on the two decisions made by the leader in the upper level. The numerical experiments show that if the decision-makers do not consider the peri-implementation capacity reduction, the resulting set of selected projects and their construction schedule can lead to significant travel delay cost for the road users. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Transport Project selection Project scheduling Bi-level program active-set algorithm

来源：评论

学校读者我要写书评

暂无评论

Design of moth flame optimization heuristics for integrated power plant system containing stochastic wind

引用

APPLIED SOFT COMPUTING 2021年 104卷 107193-107193页

作者： Khan, Babar Sattar Raja, Muhammad Asif Zahoor Qamar, Affaq Chaudhary, Naveed Ishtiaq Univ Engn & Technol Peshawar Dept Elect Energy Syst Engn USPCAS E Peshawar Khyber Pakhtunk Pakistan COMSATS Univ Islamabad Dept Elect & Comp Engn Campus Attock Attock Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Int Islamic Univ Dept Elect Engn Islamabad Pakistan

In this investigation, nature-inspired heuristic strategy exploiting moth flame optimization (MFO) algorithm combined with active-set algorithm (ASA), interior point algorithm (IPA) and sequential quadratic programming (SQP) are presented to take care of the enhancement issues of economic load dispatch (ELD) problem involving valve point loading effect (VPLE) and stochastic wind (SW). The strength of MFO algorithm is used as a global search mechanism that explore and exploit the entire search space while ASA, IPA and SQP are responsible for refinement of local optimum. The performance of the design system is based on 40 generating units including 37 thermal and 3 wind power units and is evaluated to verify the effectiveness of the scheme. The worth of the design integrated heuristic of MFO algorithm is endorsed through outcomes of the state of the art counterpart solvers in case of ELD problems integrated with wind power units in terms of cost minimization and computational complexity parameters. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Moth flame optimization algorithm Sequential quadratic programming active-set algorithm Interior-point algorithm Economic load dispatch Integrated power systems

来源：评论

学校读者我要写书评

暂无评论

DESIGN OF NEURO-SWARMING HEURISTIC SOLVER FOR MULTI-PANTOGRAPH SINGULAR DELAY DIFFERENTIAL EQUATION

引用

FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2021年第5期29卷 2140022-2140022页

作者： Sabir, Zulqurnain Baleanu, Dumitru Raja, Muhammad Asif Zahoor Guirao, Juan L. G. Hazara Univ Dept Math & Stat Mansehra Pakistan Cankaya Univ Dept Math Ankara Turkey Inst Space Sci Magurele Romania China Med Univ China Med Univ Hosp Dept Med Res Taichung Taiwan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Yunlin 64002 Taiwan COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock 43600 Pakistan Tech Univ Cartagena Dept Appl Math & Stat Hosp Marina Cartagena 30203 Spain

This research work is to design a neural-swarming heuristic procedure for numerical investigations of Singular Multi-Pantograph Delay Differential (SMP-DD) equation by applying the function approximation aptitude of Artificial Neural Networks (ANNs) optimized efficient swarming mechanism based on Particle Swarm Optimization (PSO) integrated with convex optimization with active set (AS) algorithm for rapid refinements, named as ANN-PSO-AS. A merit function (MF) on mean squared error sense is designed by using the differential ANN models and boundary condition. The optimization of this MF is executed with the global PSO and local search AS approaches. The planned ANN-PSO-AS approach is instigated for three different SMP-DD model-based equations. The assessment with available standard results relieved the effectiveness, robustness and precision that is further authenticated through statistical investigations of Variance Account For, Root Mean Squared Error, Semi-Interquartile Range and Theil's inequality coefficient performances.

关键词： Multi-pantograph Systems Particle Swarm Optimization Neural Networks active-set algorithm Numerical Computing Statistical Measures

来源：评论

学校读者我要写书评

暂无评论

Numerical treatment of nonlinear singular Flierl-Petviashivili systems using neural networks models

引用

NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 2371-2394页

作者： Raja, Muhammad Asif Zahoor Khan, Junaid Ali Zameer, Aneela Khan, Najeeb Alam Manzar, Muhammad Anwaar COMSATS Inst Informat Technol Dept Elect Engn Attock Pakistan HITEC Univ Texila Dept Comp Sci & Engn Rawalpindi Pakistan Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Islamabad Pakistan Univ Karachi Dept Math Sci Karachi 75270 Pakistan Hamdard Univ Hamdard Inst Engn & Technol Islamabad Pakistan

In this study, new intelligent computing methodologies have been developed for highly nonlinear singular Flierl-Petviashivili (FP) problem having boundary condition at infinity by exploiting three different neural network models integrated with active-set algorithm (ASA). A modification in the modeling is introduced to cater the singularity, avoid divergence in results for unbounded inputs and capable of dealing with strong nonlinearity. Three models have been constructed in an unsupervised manner for solving the FP equation using log-sigmoid, radial basis and tan-sigmoid transfer functions in the hidden layers of the network. The training of adaptive adjustable variables of each model is carried out with a constrained optimization technique based on ASA. The proposed models have been evaluated on three variants of the two FP equations. The designed models have been examined with respect to precision, stability and complexity through statistics.

关键词： Flierl-Petviashivili problem Artificial neural networks Radial basis function Nonlinear ODEs Nonlinear singular systems active-set algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：