Search Results - (( variable equation modeling algorithm ) OR ( parameter optimization method algorithm ))

Parameter extraction of single, double, and three diodes photovoltaic model based on guaranteed convergence arithmetic optimization algorithm and modified third order Newton Raphson methods by Mohammed Ridha, Hussein, Hizam, Hashim, Mirjalili, Seyedali, Othman, Mohammad Lutfi, Ya'acob, Mohammad Effendy, Ahmadipour, Masoud

“…Numerous research have reviewed and presented approaches for figuring out the PV models parameter optimization problem in the literature. …”
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Thin Film Roughness Optimization In The Tin Coatings Using Genetic Algorithms by Fauzi, Nur Faiqah, Mohamad Jaya, Abdul Syukor, Mohammad Jarrah, Mu’ath Ibrahim, Akbar, Habibullah

“…Genetic algorithms were used in the optimization work of the coating process to optimize the coating roughness parameters. …”
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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 by Mohamad Jaya, Abdul Syukor, Muhamad, Mohd Razali, Abd Rahman, Md Nizam, Mohammad Jarrah, Mu'ath Ibrahim, Hasan Basari, Abd Samad

“…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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Comparison of conjugate gradient methods for developing the multiple linear regression model for rice production in Malaysia / Nur Atikah Mustapha by Mustapha, Nur Atikah

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Tuning Factor the Single Input Fuzzy Logic Controller to Improve the Performances of Depth Control for Underwater Remotely Operated Vehicle by Mohd Aras, Mohd Shahrieel, Anuar, Mohamed Kassim, Khamis, Alias

“…The variable parameter for SIFLC is tuned by Particle Swarm Optimization (PSO) algorithm. …”
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Variable Neighborhood Descent and Whale Optimization Algorithm for Examination Timetabling Problems at Universiti Malaysia Sarawak by Emily Sing Kiang, Siew

“…A constructive algorithm was developed to generate an initial feasible solution, which was subsequently refined using two primary approaches to evaluate their efficiency: Iterative Threshold Pipe Variable Neighborhood Descent (IT-PVND), and a modified Whale Optimization Algorithm (WOA). …”
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Power System State Estimation In Large-Scale Networks by NURSYARIZAL MOHD NOR, NURSYARIZAL

“…Also the WLS algorithm is modified to include Unified Power Flow Controller (UPFC) parameters. …”
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Enhancing reservoir simulation models with genetic algorithm optimized neural networks across diverse climatic zones / Saad Mawlood Saab by Saad Mawlood , Saab

“…The optimizer algorithm (i.e., GA) determines the optimal input variables and internal parameters in the prediction models. …”
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Intelligence Integration Of Particle Swarm Optimization And Physical Vapour Deposition For Tin Grain Size Coating Process Parameters by Abdul Syukor, Mohamad Jaya, Mu*ath Ibrahim, Mohammad Jarrah, Mohd Asyadi Azam, Mohd Abid, Mohd Razali, Muhamad

“…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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Optimization of Microbial Electrolysis Cell for Sago Mill Wastewater Derived Biohydrogen via Modeling and Artificial Neural Network by Mohamad Afiq, Mohd Asrul

“…Model validity describes the first sub-objective, which is to solve the complexity of the nonlinear interaction of multiple MEC input variables related to the hydrogen production rate response using artificial neural networks (ANN) before validating the mathematical modeling results by comparing experimental data with the predicted substrate concentration profile and hydrogen production rate profile based on the re-estimated input values of the model parameters using single-objective optimization based on the nonlinear convex method using gradient descent algorithm. …”
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Operational matrix based on orthogonal polynomials and artificial neural networks methods for solving fractal-fractional differential equations by Shloof, Aml Melad Asan

“…This discovery indicates convergence toward optimal model coefficients as the learning process advances. …”
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Evaluation of lightning return stroke current using measured electromagnetic fields by Mahdi, Izadi

“…Moreover, the current wave shapes are validated with measured field at other observation point when the determined current parameters and the proposed direct procedure algorithms are applied. …”
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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data by Oyebayo, Olaniran Ridwan

“…BRF algorithm combines the strengths of random subset and greedy selection procedures in creating new maximal ordered variable relevance weights. …”
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Modeling and Optimization of Tapered Rectangular Thin-walled Columns Subjected to Oblique Loading for Impact Energy Absorption by Siti Aishah, Rusdan, Tarlochan, Faris, Mohamad Rusydi, Mohamad Yasin

“…The optimal design is obtained by using the constrained nonlinear multivariable optimization algorithm provided by MATLAB. …”
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SURE-Autometrics algorithm for model selection in multiple equations by Norhayati, Yusof

“…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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Model predictive control based on Lyapunov function and near state vector selection of four-leg inverter / Abdul Mannan Dadu by Abdul Mannan, Dadu

“…The proposed control algorithm takes advantage of a predefined Lyapunov control law which minimizes the required calculation time by the Lyapunov model equations just once in each control loop to predict future variables. …”
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Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925) by Kamarudin, Nur Azulia

“…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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Assessing the simulation performances of multiple model selection algorithm by Yusof, Norhayati, Ismail, Suzilah, Tuan Muda, Tuan Zalizam

“…The capability of the algorithm in finding the true specification of multiple models is measured by the percentage of simulation outcomes.Overall results show that the algorithm has performed well for a model with two equations.The findings also indicated that the number of variables in the true models affect the algorithm performances. …”
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Optimization of turning parameters using genetic algorithm method by Shah Izwandi, Mohd Zawawi

“…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). …”
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Parameters optimization of surface grinding process with particles swarm optimization, gravitational search, and sine cosine algorithms: a comparative analysis by Asrul, Adam

“…The efficiency of the three algorithms are evaluated and compared with previous results obtained by other optimization methods on similar studies. …”
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