Search Results - (( parameter optimization method algorithm ) OR ( variable regression models algorithm ))
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A hybrid genetic algorithm and linear regression for prediction of NOx emission in power generation plant
Published 2023Subjects:Conference paper -
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing
Published 2024“…Firefly algorithm outperformed the other metaheuristic algorithms used to solve this proposed hybrid artificial intelligence model regarding parameter sensitivity. …”
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Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm
Published 2022“…Therefore, in this paper, we enhance the GMDH model by implementing the ABC algorithm to optimize the parameter of partial description GMDH model with some transfer functions, namely polynomial, radial basis, sigmoid and hyperbolic tangent function. …”
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Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization
Published 2022“…The ANN model is further improved using GA and PSO. Each algorithm has its own parameters and is further optimized using RSM. …”
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Proving the efficiency of alternative linear regression model based on mean square error (MSE) and average width using aquaculture data
Published 2019“…The main objective of this paper is to compare mean square error (MSE) and the average width between alternative linear regression models and linear regression model. The alternative method in this study is a combination of four methods, namely multiple linear regression method, the bootstrap method, a robust regression method and fuzzy regression through the construction of algorithms by using SAS software. …”
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Proving the eficiency of Alternative Linear regression Model Based on Mean Square Error (MSE) and average width using aquaculture data
Published 2019“…The main objective of this paper is to compare mean square error (MSE) and the average width between alternative linear regression models and linear regression model. The alternative method in this study is a combination of four methods, namely multiple linear regression method, the bootstrap method, a robust regression method and fuzzy regression through the construction of algorithms by using SAS software. …”
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Multivariate Based Analysis of Methane Adsorption Correlated to Toc and Mineralogy Impact from Different Shale Fabrics
Published 2021“…The PLS is a mathematical optimization method and is employed in this study along with SVR and random forest method due to its ability to incorporate the correlation between the independent variables, which decrease the influence of noise, and identifies the system information in linear regression study. …”
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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
Published 2018“…BRF algorithm combines the strengths of random subset and greedy selection procedures in creating new maximal ordered variable relevance weights. …”
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Comparison between specifications of linear regression and spatial-temporal autoregressive models in mass appraisal valuation for single storey residential property
Published 2013“…Furthermore, various spatial, temporal and spatio-temporal neighbourhood and weighting schemes, optimization algorithms and lag and error modelling scenarios were created and tested with the data. …”
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Model input and structure selection in multivariable dynamic modeling of batch distillation column pilot plant / Ilham Rustam
Published 2015“…Further, after a careful investigation into the OLS algorithm, it was shown that the ERR technique which is an essential part of the algorithm to reach model parsimony, has led the resultant model to select an incorrect model terms albeit some improvement in model selection criteria and validation method adopted in this study. …”
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Modeling of flood water level prediction using NNARX / Fazlina Ahmat Ruslan
Published 2015“…Further, after a careful investigation into the OLS algorithm, it was shown that the ERR technique which is an essential part of the algorithm to reach model parsimony, has led the resultant model to select an incorrect model terms albeit some improvement in model selection criteria and validation method adopted in this study. …”
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Landslide susceptibility mapping using decision-tree based chi-squared automatic interaction detection (CHAID) and logistic regression (LR) integration
Published 2014“…LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. …”
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Hospital site suitability assessment using three machine learning approaches: evidence from the Gaza strip in Palestine
Published 2021“…The study aims to find an optimal solution for a suitable hospital location through suitability mapping using relevant environmental, topographic, and geodemographic parameters and their variable criteria. …”
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Optimization of turning parameters using genetic algorithm method
Published 2008“…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). …”
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Undergraduates Project Papers -
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Parameters optimization of surface grinding process with particles swarm optimization, gravitational search, and sine cosine algorithms: a comparative analysis
Published 2018“…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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Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization
Published 2014“…Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. …”
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Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition
Published 2021“…These methods are also utilized to produce a consistent model in terms of variable selection and asymptotically normal estimates and address the multicollinearity problem when it exists between the predictor variables. …”
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