Search Results - (( rate estimation means algorithm ) OR ( parameter optimization methods algorithm ))*

A comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module's model by Muhsen D.H., Ghazali A.B., Khatib T., Abed I.A.

“…Algorithms; Diodes; Errors; Iterative methods; Least squares approximations; Mean square error; Optimization; Parameter estimation; Parameter extraction; Photovoltaic cells; Coefficient of determination; Differential Evolution; Electromagnetism-like algorithm; Hybrid evolutionary algorithm; Photovoltaic; Photovoltaic modules; Root mean square errors; Single-diode models; Evolutionary algorithms; algorithm; comparative study; electromagnetic method; estimation method; experimental design; numerical method; parameterization; performance assessment; photovoltaic system…”

Parameters extraction of double diode photovoltaic module's model based on hybrid evolutionary algorithm by Muhsen D.H., Ghazali A.B., Khatib T., Abed I.A.

“…Algorithms; Diodes; Errors; Extraction; Iterative methods; Mean square error; Optimization; Parameter estimation; Parameter extraction; Photovoltaic cells; Differential evolution algorithms; Diode modeling; Electromagnetism-like algorithm; Fast convergence speed; Hybrid evolutionary algorithm; IV characteristics; Photovoltaic model; Root mean square errors; Evolutionary algorithms…”

Extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm by Muhsen D.H., Ghazali A.B., Khatib T., Abed I.A.

“…Algorithms; Evolutionary algorithms; Mean square error; Optimization; Parameter estimation; Photovoltaic cells; Coefficient of determination; DEAM; Five parameters; Hybrid differential evolution; Improved differential evolutions; IV characteristics; Photovoltaic modules; Root mean square errors; Iterative methods; algorithm; artificial intelligence; electromagnetic method; error analysis; experimental study; photovoltaic system…”

Cutting temperature and surface roughness optimization in CNC end milling using multi objective genetic algorithm by Al Hazza, Muataz, Adesta, Erry Yulian Triblas, Superianto, M. Y., Riza, Muhammad

“…Thus, developing a model for estimating the cutting parameters and optimizing this model to minimize the cutting temperatures and surface roughness becomes utmost important to avoid any damage to the quality surface.This paper presents the development of new models and optimizing these models of machining parameters to minimize the cutting temperature in end milling process by integrating the genetic algorithm (GA) with the statistical approach. …”
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm by Reza M.S., Hannan M.A., Mansor M., Ker P.J., Rahman S.A., Jang G., Mahlia T.M.I.

“…In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. …”

An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models by Jalil-Masir H., Fattahi R., Ghanbari-Adivi E., Asadi Aghbolaghi M., Ehteram M., Ahmed A.N., El-Shafie A.

Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass by Mohammad, Saleem Ethaib, Omar, Rozita, Mustapa Kamal, Siti Mazlina, Awang Biak, Dayang Radiah, S., Syafiie

“…Specifically, it was used to determine the optimum number of neurons in the hidden layer and the best value of the learning rate of the ANN model. The optimization method includes minimizing the fitness function mean absolute error that was found to be 0.0176. …”
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Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources by Kumar, A., Ridha, S., Narahari, M., Ilyas, S.U.

“…The statistical error estimation exhibits a mean absolute error of 11.5, and root mean squared error of 0.87. …”
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Ant colony optimization and genetic algorithm models for suspended sediment discharge estimation for gorgan-river, Iran by Mohammad Reza Pour, Omolbani

“…New models based on artificial intelligence models, namely; Ant Colony Optimization (ACO) and Genetic Algorithm (GA) are now being used more frequently to solve optimization problems. …”
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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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Robust estimation methods for fixed effect panel data model having block-concentrated outliers by Abu Bakar @ Harun, Nor Mazlina

“…Firstly, we proposed robust panel data transformation to be performed around the MM-estimate of location as an alternative to the non-robust centering by the mean. …”
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An improvement of stochastic gradient descent approach for mean-variance portfolio optimization problem by S. W. Su, Stephanie, Kek, Sie Long

“…,e aim is to fasten the convergence rate of the Adam algorithm. ,is improvement is termed as Adam with standard error (AdamSE) algorithm. …”
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Robust remote heart rate estimation from multiple asynchronous noisy channels using autoregressive model with Kalman filter by Nooralishahi, Parham, Loo, Chu Kiong, Shiung, Liew Wei

“…We propose a novel algorithm to estimate heart rate. Also, it can differentiate between a photo of a human face and an actual human face meaning that it can detect false signals and skip them. …”
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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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Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization by Nur Iffah, Mohamed Azmi

“…Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. …”
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Optimization of turning parameters using ant colony optimization by Mohamad Nazri, Semoin

“…The project objectives are to develop Ant Colony Optimization (ACO) algorithm for CNC turning process and to optimize turning parameters for minimized production cost per unit. …”
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Parameter estimation in double exponential smoothing using genetic algorithm / Foo Fong Yeng, Lau Gee Choon and Zuhaimy Ismail by Foo, Fong Yeng, Lau, Gee Choon, Ismail, Zuhaimy

“…Trial and error often serves as the best method to determine the parameter. Therefore, a good optimization technique is required for identify the best parameter in minimizing the forecast errors. …”
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Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation by Premkumar M., Ravichandran S., Hashim T.J.T., Sin T.C., Abbassi R.

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Application Of Genetic Algorithms For Robust Parameter Optimization by Belavendram, N.

“…Genetic algorithms (GA) are fairly recent in this respect but afford a novel method of parameter optimization. …”
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