Search Results - (( parameter estimation using algorithm ) OR ( (variable OR variables) learning based algorithm ))

A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

“…Firefly algorithm outperformed the other metaheuristic algorithms used to solve this proposed hybrid artificial intelligence model regarding parameter sensitivity. …”
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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm by Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.

“…This study proposed an improved parameter estimation procedure for PEMFCs by using the GOOSE algorithm, which was inspired by the adaptive behaviours found in geese during their relaxing and foraging times. …”

Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms by Abed M., Imteaz M.A., Ahmed A.N., Huang Y.F.

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.

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Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application by Hossain Lipu M.S., Hannan M.A., Hussain A.

Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications by Shanmugam Y., Narayanamoorthi R., Ramachandaramurthy V.K., Bernat P., Shrestha N., Son J., Williamson S.S.

“…This article proposes an effective machine learning (ML) approach to achieve the optimal design of the charging track, considering the cross-coupling effect. The algorithm not only aids in estimating the infrastructure cost of the charging lane but also predicts optimal design parameters using trained data. …”

Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
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Price prediction model of green building based on machine learning algorithms / Nur Syafiqah Jamil by Jamil, Nur Syafiqah

“…The selection features involved were based on Experiment 1 which included 17 IVs (all features) without excluding the most significant variable for this research.…”
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Feedback error learning control for underactuated acrobat robot with radial basis funtion based FIR filter by Zainul Azlan, Norsinnira, Yamaura, Hiroshi

“…Besides, it also formulates a simple approach for FEL feedforward controller structure by employing the inverse dynamic model of the plant with physical parameters. The RBFN based FIR filter is used in obtaining the estimation of the state variables to produce an ideal feedforward control input. …”
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Sensorless induction motor speed control for electric vehicles using enhanced hybrid flux estimator with ann-ifoc controller by Sepeeh, Muhamad Syazmie

“…The results of the ANN-IFOC hybrid estimator were obtained in four cases, which were 1) constant high and low speeds, 2) constant speed against parameter variation, 3) variable speed, and 4) variable load torque disturbances. …”
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Optimization of Lipase Catalysed Synthesis of Sugar Alcohol Esters Using Taguchi Method and Neural Network Analysis by Adnani, Seyedeh Atena

“…Various feedforward neural networks were performed using different learning algorithms. The best algorithm was found to be Levenberg–Marquardt (LM) for a network composed of two hidden layers with six and seven neurons in the first and second layers, respectively for xylitol stearate and xylitol palmitate and also seven and five neurons in the first and second layers for xylitol caprate, with hyperbolic tangent sigmoid transfer function. …”
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Forecasting of meteorological drought using ensemble and machine learning models by Pande C.B., Sidek L.M., Varade A.M., Elkhrachy I., Radwan N., Tolche A.D., Elbeltagi A.

“…The SPI is a popular method for estimating the drought analysis and has been used everywhere at global level. …”

Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy by Hossain, Ishtiaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

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An optimized ensemble for predicting reservoir rock properties in petroleum industry by Kenari, Seyed Ali Jafari

“…A lot of researches have been done to predict the reservoir parameters using well log data through applying various methods. …”
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Experimental analysis and data-driven machine learning modelling of the minimum ignition temperature (MIT) of aluminium dust by Arshad, U., Taqvi, S.A.A., Buang, A.

“…Based on the statistical nature of the dust explosions and controlling parameters, this study uses data-driven modelling approaches. …”
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Experimental analysis and data-driven machine learning modelling of the minimum ignition temperature (MIT) of aluminium dust by Arshad, U., Taqvi, S.A.A., Buang, A.

“…Based on the statistical nature of the dust explosions and controlling parameters, this study uses data-driven modelling approaches. …”
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Grid-based remotely sensed hydrodynamic surface runoff model using emissivity coefficient / Jurina Jaafar by Jaafar, Jurina

“…The development of the model strongly depends on the physical based parameters, examples of physical parameters that include roughness Manning’s n, hydraulic conductivity, soil depth, river geometry and the surface land cover. …”
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Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition by Ali Yahya, Anwar

“…The current dialogue act recognition models, namely cue-based models, are based on machine learning techniques, particularly statistical ones. …”
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Support vector machine and its applications for linear and nonlinear regression in the presence of outliers of high dimensional data by Sleabi, Waleed Dhhan

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Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition by Yahya, Anwar Ali, Mahmod, Ramlan, Ramli, Abd Rahman

“…The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. Furthermore, the dynamic Bayesian network's random variables are constituted from sets of lexical cues selected automatically by means of a variable length genetic algorithm, developed specifically for this purpose. …”
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