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Bio-inspired computational heuristics for Sisko fluid flow and heat transfer models

APPLIED SOFT COMPUTING 2018年 71卷 622-648页

作者： Raja, Muhammad Asif Zahoor Mehmood, Ammara Rehman, Ateeq ur Khan, Asifullah Zameer, Aneela COMSATS Inst Informat Technol Dept Elect Engn Attock Pakistan Pakistan Inst Engn & Appl Sci Dept Elect Engn Islamabad Pakistan Preston Univ Kohat Dept Math Islamabad Campus Islamabad Pakistan Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Islamabad Pakistan

A novel approach of integrated meta-heuristic computing is designed for Sisko fluid dynamics (SFD) system using artificial neural networks (ANN) based differential equation models being optimized with exploration capabilities of Genetic algorithms (GAs) and efficient convergence of active -set algorithm (ASA) for different scenarios based on variation of magnetic interaction parameter, stretching parameter, generalized Prandtl and Biot numbers. Governing partial differential equations (PDEs) of SFD system are converted into coupled nonlinear ordinary differential equations (ODEs) through similarity variables and then ANN based models are constructed for the transformed problem. These networks are trained globally using GAs with fine tuning by ASA for speedy optimization. Accurate and consistent convergence of this scheme is established by comparison of results with standard Adams numerical approach and further validated through statistical performance indices. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Artificial neural networks Integrated computing Genetic algorithms active-set algorithm Sisko fluid dynamics

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A new optimized Stockwell transform applied on synthetic and real non-stationary signals

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DIGITAL SIGNAL PROCESSING 2015年 46卷 226-238页

作者： Moukadem, Ali Bouguila, Zied Abdeslam, Djaffar Ould Dieterlen, Alain Univ Haute Alsace MIPS Lab Mulhouse France

The aim of this paper is to improve the energy concentration of the Stockwell transform (S-transform) in the time-frequency domain. A modified S-transform is proposed with several parameters to control the width of a hybrid Gaussian window. A constrained optimization problem is proposed based on an energy concentration measure as objective function and inequalities constraints to define the bounds of the Gaussian window. An active-set algorithm is applied to resolve the optimization problem. The optimization of the energy concentration in the time-frequency plane can lead to more reliable applications for non-stationary signals. The simulation results show a significant improvement of the proposed methodology most notably in the presence of noise comparing with the standard S-transform and existing modified S-transform in the literature. Moreover, comparison with other known time-frequency transforms such as Short-time Fourier transform (STFT) and smoothed-pseudo Wigner-Ville distribution (SPWVD) is also performed and discussed. The proposed S-transform is tested also on real non-stationary signals through an example of split detection in heart sounds. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Time-frequency Stockwell transform Energy concentration active-set algorithm Heart sounds

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

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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

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Design of bio-inspired computational intelligence technique for solving steady thin film flow of Johnson-Segalman fluid on vertical cylinder for drainage problems

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2016年 60卷 59-75页

作者： Raja, Muhammad Asif Zahoor Shah, Fiaz Hussain Khan, Abdul Ahad Khan, Najeeb Alam COMSATS Inst Informat Technol Dept Elect Engn Attock Campus Attock 43600 Punjab Pakistan Univ Karachi Dept Math Sci Karachi 75270 Pakistan

In the present study, bio-inspired computing technique is designed for solving governing mathematical relation for steady thin film flow of Johnson-Segalman fluid on vertical cylinder for drainage problems using Artificial Neural Networks (ANNs), genetic algorithms (GAs) and active-set algorithm (ASA). The strength of ANN modeling is exploited for the transformed equation of drainage problem which is derived from original partial differential equation using similarity transform. Training of design parameter of ANNs is carried out with evolutionary computing approach based on GAs hybrid with ASA for rapid local convergence. Design scheme is evaluated for number of cases of all four scenarios of drainage problem based on variations in Stokes number, Weissenberg number, ratio of viscosities, and slip parameters. Comparison of the results is made with Adams numerical method for each case in order to validate the accuracy of the proposed scheme. Results of statistical analysis in terms of performance measures based on mean, standard deviation, mean absolute deviation, root mean square error and Nash-Sutcliffe efficiency as well as their global variations further established the worth of the given scheme for each variant of drainage problem. (C) 2015 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Thin film flow Non-Newtonian fluids Computational fluid dynamics Artificial Neural Networks Genetic algorithms active-set algorithm

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REGULARIZED NONNEGATIVE MATRIX FACTORIZATION: GEOMETRICAL INTERPRETATION AND APPLICATION TO SPECTRAL UNMIXING

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2014年第2期24卷 233-247页

作者： Zdunek, Rafal Wroclaw Univ Technol Dept Elect PL-50370 Wroclaw Poland

Nonnegative Matrix Factorization (NMF) is an important tool in data spectral analysis. However, when a mixing matrix or sources are not sufficiently sparse, NMF of an observation matrix is not unique. Many numerical optimization algorithms, which assure fast convergence for specific problems, may easily get stuck into unfavorable local minima of an objective function, resulting in very low performance. In this paper, we discuss the Tikhonov regularized version of the Fast Combinatorial NonNegative Least Squares (FC-NNLS) algorithm (proposed by Benthem and Keenan in 2004), where the regularization parameter starts from a large value and decreases gradually with iterations. A geometrical analysis and justification of this approach are presented. The numerical experiments, carried out for various benchmarks of spectral signals, demonstrate that this kind of regularization, when applied to the FC-NNLS algorithm, is essential to obtain good performance.

关键词： blind source separation nonnegative matrix factorization active-set algorithm regularized NMF polytope approximation

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Efficient methods for solving the Stokes problem with slip boundary conditions

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MATHEMATICS AND COMPUTERS IN SIMULATION 2018年 145卷 114-124页

作者： Kucera, Radek Haslinger, Jaroslav Satek, Vaclav Jarosova, Marta VSB TU Ostrava 17 Listopadu 15-2172 Ostrava 70833 Czech Republic

The paper deals with the Stokes flow with the threshold slip boundary conditions. A finite element approximation of the problem leads to the minimization of a non-differentiable energy functional subject to two linear equality constraints: the impermeability condition on the slip part of the boundary and the incompressibility of the fluid. Eliminating the velocity components, one gets the smooth dual functional in terms of three Lagrange multipliers. The first Lagrange multiplier regularizes the problem. Its components are subject to simple bounds. The other two Lagrange multipliers treat the impermeability and the incompressibility conditions. The last Lagrange multiplier represents the pressure in the whole domain. The solution to the dual problem is computed by an active set strategy and a path-following variant of the interior-point method. Numerical experiments illustrate computational efficiency. (C) 2016 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Stokes problem Slip boundary condition active-set algorithm Interior-point method

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Intelligent computing approach to analyze the dynamics of wire coating with Oldroyd 8-constant fluid

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NEURAL COMPUTING & APPLICATIONS 2019年第3期31卷 751-775页

作者： Munir, Annum Manzar, Muhammad Anwaar Khan, Najeeb Alam Raja, Muhummad Asif Zahoor Capital Univ Sci & Technol Dept Math Islamabad Pakistan Preston Univ Kohat Dept Math Islamabad Campus Kohat Pakistan Hamdard Univ Hamdard Inst Engn & Technol Islamabad Pakistan Univ Karachi Dept Math Sci Karachi 75270 Pakistan COMSATS Inst Informat Technol Dept Elect Engn Attock Pakistan

In the study, intelligent computing technique is developed for solving the nonlinear system for wire coating analysis with the bath of Oldroyd 8-constant fluid having pressure gradient using feedforward artificial neural networks (ANNs), evolutionary computing, active-set algorithm (ASA) and their hybrid. Original partial differential equations of wire coating process are converted to nonlinear ordinary differential equation (NL-ODEs) in dimensionless form using similarity transformation. Strength of ANNs is exploited to develop mathematical model of the transformed equations by defining an unsupervised error. Training of design variables of the network is carried out globally using evolutionary computing techniques based on genetic algorithms (GAs) hybrid with ASA for rapid local convergence. Design scheme is applied to analyze the dynamics of the problem for number of variants based on dilatant constant, the pseudoplastic constant, the pressure gradient, shear stress under the effect of viscosity parameter and varying the coating thickness of the polymer. Results of the proposed method are compared with standard numerical solver for NL-ODEs based of Adams method to establish its correctness. Reliability of the method is further validated through the results of statistics based on different performance measures for accuracy and computational complexity.

关键词： Wire coating analysis Fluid dynamics Nonlinear systems Artificial neural network Genetic algorithms active-set algorithm

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

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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)

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Applicability comparison of different algorithms for ambient signal based load model parameter identification

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年 111卷 382-389页

作者： Wang, Ying Lu, Chao Zhang, Xinran Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

The time-varying characteristic of the power load can be tracked with the ambient signal based load model parameter identification method proposed recently. Meanwhile, the small fluctuations of the ambient signal bring higher requirement for optimization algorithm as well. The interior-point algorithm (IPA) and the activeset algorithm (ASA) are two typical traditional nonlinear constrained optimization algorithms, while the differential evolution algorithm (DEA) is an excellent heuristic search algorithm. Besides, the grid search algorithm (GSA) is a violent search algorithm, which is also an effective method considering the fact that the real load model parameters are unknown in practical power system. Based on the review of the basic idea of the four algorithms, the applicability of the four algorithms is comparatively assessed for ambient signal based load model parameter identification. Their performance is compared using ambient data from both simulation and field phasor measurement unit (PMU) in China Southern Power Grid considering the identification accuracy, calculation time, and the robustness to measurement error and algorithm parameters. Some general conclusions are drawn from the analysis for several cases.

关键词： Ambient signal based load model parameter identification active-set algorithm Differential evolution algorithm Grid search algorithm Interior-point algorithm

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

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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

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