Search Results - (( variable estimation using algorithm ) OR ( using optimization means algorithm ))
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Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
Published 2025“…Therefore, it is recommended to utilize the prediction algorithms within the range of input variables employed in this investigation for optimal results. ? …”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing
Published 2024“…While the firefly algorithm solution is superior, it has a higher time complexity compared to other algorithms used when there are more hidden layers and neurons. …”
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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
Published 2025“…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. …”
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Battery remaining useful life estimation based on particle swarm optimization-neural network
Published 2024“…In the evaluation of the proposed method, the effectiveness is assessed using the metrics of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). …”
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Production quantity estimation using an improved artificial neural network
Published 2015“…The performance of NNBP can be analyzed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). …”
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ADAPTIVE MODULATION AND CODING FOR WIMAX SYSTEMS
Published 2006“…First the SNR estimation using the three algorithms is implemented in Wimax system, based on the transmission protocols specified in Wimax. …”
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
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Harmony Search algorithm-based gasoline consumption modeling for Indonesia
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Maximum 2-satisfiability in radial basis function neural network
Published 2020“…The results obtained are analysed using the ratio of satisfied clause (RSC), the root means square error (RMSE), and CPU time. …”
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Development Of Two New Auxiliary Information Control Charts, And Economic And Economic-Statistical Designs Of Several Auxiliary Information Control Charts
Published 2020“…The first objective of this thesis is to develop the run sum X - AI (RS X - AI) chart for monitoring the process mean. Optimal parameters computed using the optimization algorithms developed and the step-by-step approach for constructing the optimal RS - AI chart are provided in this thesis. …”
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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 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. Typically, the alternative method optimized by minimizing the mean square error (MSE) and average width. …”
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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 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. Typically, the alternative method optimized by minimizing the mean square error (MSE) and average width. …”
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Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption
Published 2015“…The incremental back propagation algorithm demonstrated the best results and which has been used as learning algorithm for ANN in combination with Genetic Algorithm in the optimization. …”
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Optimization of Lipase Catalysed Synthesis of Sugar Alcohol Esters Using Taguchi Method and Neural Network Analysis
Published 2011“…In this system,similar insolvent system, three methods including one variable at a time, Taguchi method and ANN were used for optimization and prediction of percentage of conversion. …”
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Optimization of Microbial Electrolysis Cell for Sago Mill Wastewater Derived Biohydrogen via Modeling and Artificial Neural Network
Published 2023“…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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Identification of debris flow initiation zones using topographic model and airborne laser scanning data
Published 2017“…Conditioning parameters were numerically optimized to identify the arbitrarily maximum model basis function for eleven variables, using MARSplines analysis (algorithm). …”
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An optimized ensemble for predicting reservoir rock properties in petroleum industry
Published 2013“…A total of 3695 data points from the 5 wells having conventional well log data and core data were used. Threeperformance measurements for analysing and comparing the predicted results and target values including correlation coefficient (R),Root Mean Squared Error (RMSE) and related RMSE were selected. …”
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