Search Results - (( using vector method algorithm ) OR ( parameter simulation model algorithm ))

Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method by Abbaszadeh M., Soltani-Mohammadi S., Ahmed A.N.

“…Copper deposits; Deposits; Geology; Learning algorithms; Mineralogy; Static Var compensators; Support vector machines; Three dimensional computer graphics; Alteration zones; Grid search; Grid-search method; Mineralization zone; Model Selection; Particle swarm optimization algorithm; Penalty parameters; Performance; Support vector classifiers; Support vectors machine; Particle swarm optimization (PSO); accuracy assessment; algorithm; classification; computer simulation; copper; geological survey; mineral alteration; mineralization; numerical model; ore deposit; parameterization; performance assessment; porphyry; resource assessment; support vector machine; three-dimensional modeling; Iran…”

The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction by Adnan R.M., Kisi O., Mostafa R.R., Ahmed A.N., El-Shafie A.

A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant by Chun T.S., Malek M.A., Ismail A.R.

Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Pr... by Sammen S.S., Ehteram M., Sheikh Khozani Z., Sidek L.M.

“…A coati optimization algorithm is introduced to select input scenarios. …”

A Hybrid Gini PSO-SVM Feature Selection: An Empirical Study of Population Sizes on Different Classifier by Noormadinah Allias, Megat NorulAzmi Megat Mohamed Noor, Mohd. Nazri Ismail, Kim de Silva, (UniKL MIIT)

“…Deriving from previous experiments, we extended our work by investigating the effect of population sizes from our proposed method of feature selection on different learning classifier algorithms using Random Forest, Voting, Decision Tree, Support Vector Machine and Stacking. …”

A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant by Ting, Sie Chun, Abdul Malik, Marlinda, Ismail, Amelia Ritahani

“…In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a wellestablished method – namely the least-square support vector machine (LS-SVM) as a baseline model. …”
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Reproducing kernel Hilbert space method for cox proportional hazard model by Abdul Manaf, Nur'azah

“…This algorithm is used to determine the vector i a that enables us to find the optimal parameters of ƒ(x)which is simplified as F(x)= ∑aᵢK(x,xᵢ) . …”
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A new history matching sensitivity analysis framework with random forests and Plackett-Burman design by Aulia, A., Jeong, D., Mohd Saaid, I., Shuker, M.T., El-Khatib, N.A.

“…Once the samples are ready, the parameters' input values and the target vector (i.e. the history matching error vector) are used to construct a Random Forests model. …”
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LSSVM parameters tuning with enhanced artificial bee colony by Mustaffa, Zuriani, Yusof, Yuhanis

“…To guarantee its convincing performance, it is crucial to select an appropriate technique in order to obtain the optimized hyper-parameters of LSSVM algorithm.In this paper, an Enhanced Artificial Bee Colony (eABC) is used to obtain the ideal value of LSSVM’s hyper parameters, which are regularization parameter, γ and kernel parameter, σ2.Later, LSSVM is used as the prediction model. …”
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Prediction Of Petroleum Reservoir Properties Using Nonlinear Feature Selection And Ensembles Of Computational Intelligence Techniques by Anifowose, Fatai Adesina

“…Common metrics were used to evaluate the performance of the proposed models. …”
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Indirect Rotor Field Oriented Control of Induction Motor With Rotor Time Constant Estimation by Moh'd Radwan, Eyad Moh'd

“…A simple yet effective rotor time constant identification method is presented and used for updating the slip calculator used by the IRFOC algorithms. …”
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Cell by cell artificial neural network model for predicting laminar, incompressible, viscous flow by Sabir, O., Tuan Ya, T.M.Y.S.

“…A feedforward neural network architecture is applied in this research. The model is trained using Levenberg-Marquardt and Bayesian regularization backpropagation algorithms. …”
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Cell by cell artificial neural network model for predicting laminar, incompressible, viscous flow by Sabir, O., Tuan Ya, T.M.Y.S.

“…A feedforward neural network architecture is applied in this research. The model is trained using Levenberg-Marquardt and Bayesian regularization backpropagation algorithms. …”
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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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Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani by Ehsan Taslimi , Renani

“…To obtain the unknown vector of parameters of the MHTan, three heuristic optimization algorithms are employed to minimize the sum of squared residuals. …”
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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 position of reference currents is used to detect the voltage vectors surrounding the reference voltage vector in every sampling period. …”
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A New Method Of Speed Sensorless Control For Permanent Magnet Synchronous Motor by Samat, Ahmad Asri Abd

“…The performance of the proposed scheme is validated in Matlab/Simulink and obtained results are compared with conventional scheme. A simulation using MATLAB/Simulink software is conducted to investigate the feasibility of the proposed algorithm. …”
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Classification of transient disturbance using Wavelet based support vector machine / Fahteem Hamamy Anuwar by Anuwar, Fahteem Hamamy

“…The simulations will be done using the PSCAD/EMTDC software. …”
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Prediction of oil and gas pipeline failures through machine learning approaches: A systematic review by Al-Sabaeei, A.M., Alhussian, H., Abdulkadir, S.J., Jagadeesh, A.

“…This review article mainly focuses on the novelty of using machine and deep learning techniques, specifically artificial neural networks (ANNs), support vector machines (SVMs) and hybrid machine learning (HML) algorithms, for predicting different pipeline failures in the oil and gas industry. …”
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Numerical investigation of the performance of interior permanent magnet synchronous motor drive / Shahida Pervin by Shahida, Pervin

“…Thus, the MATLAB/Simulink library motor model can be used for motor drives simulation with sufficient accuracy. …”
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