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Data-based PID control of flexible joint robot using adaptive safe experimentation dynamics algorithm
Published 2021“…This paper proposes the data-based PID controller of flexible joint robot based on adaptive safe experimentation dynamics (ASED) algorithm. …”
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Parallel Diagonally Implicit Runge-Kutta Methods For Solving Ordinary Differential Equations
Published 2009“…The regions of stability are presented and numerical results of the methods are compared to the existing methods. Parallel computations show significant time reduction when solving large systems of nonstiff ordinary differential equations (ODEs). …”
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Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology
Published 2015“…Additionally,analysis of variance(ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters, genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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Intelligence Integration Of Particle Swarm Optimization And Physical Vapour Deposition For Tin Grain Size Coating Process Parameters
Published 2016“…Additionally,analysis of variance (ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters,genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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5
Study on numerical solution of a variable order fractional differential equation based on symmetric algorithm
Published 2019“…A fully symmetric classification of the boundary value problem for a class of fractional differential equations with variable sequences is determined by using a fully symmetric differential sequence sorting algorithm. …”
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SURE-Autometrics algorithm for model selection in multiple equations
Published 2016“…Thus, this study aims to develop an algorithm for model selection in multiple equations focusing on seemingly unrelated regression equations (SURE) model. …”
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Modeling and Optimization of Tapered Rectangular Thin-walled Columns Subjected to Oblique Loading for Impact Energy Absorption
Published 2013“…RS model using quadratic polynomial function is given as equation (1) below. …”
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Parallel method using MPI for solving large systems of delay differential equations
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Model order reduction method based on improved sine cosine algorithm
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Variable order variable stepsize algorithm for solving nonlinear Duffing oscillator
Published 2017“…In this research, a numerical approximation for solving the Duffing oscillator directly is introduced using a variable order stepsize (VOS) algorithm coupled with a backward difference formulation. …”
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Variable order variable stepsize algorithm for solving nonlinear Duffing oscillator
Published 2024journal::journal article -
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Numerical algorithm of block method for general second order ODEs using variable step size
Published 2017“…This paper outlines an alternative algorithm for solving general second order ordinary differential equations (ODEs). …”
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Loss reduction in distribution networks using new network reconfiguration algorithm
Published 1996“…Therefore, the solution algorithm of the proposed method can identify the most effective branch exchange operations for loss reduction, with a minimum computational efforts. …”
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925)
Published 2021“…Hence, the integration of EM algorithm estimation is applicable in improving the performance of automated model selection procedures for multiple equations models…”
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Two-point block backward differentiation formula for solving higher order ordinary differential equations
Published 2011“…Finally, this thesis zooms into the implementation of the BBDF method using the variable order algorithm for the solution of second order stiff ODEs directly. …”
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Numerical algorithm of block method for general second order ODEs using variable step size
Published 2017“…This paper outlines an alternative algorithm for solving general second order ordinary differential equations (ODEs). …”
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