Machine learning technology in biodiesel research: A review
M Aghbashlo, W Peng, M Tabatabaei… - Progress in Energy and …, 2021 - Elsevier
Biodiesel has the potential to significantly contribute to making transportation fuels more
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting
In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating
empirical mode decomposition (EMD) and least square support vector machine (LSSVM) …
empirical mode decomposition (EMD) and least square support vector machine (LSSVM) …
Feature weighting and SVM parameters optimization based on genetic algorithms for classification problems
Abstract Support Vector Machines (SVMs) are widely known as an efficient supervised
learning model for classification problems. However, the success of an SVM classifier …
learning model for classification problems. However, the success of an SVM classifier …
An adaptive multiscale ensemble learning paradigm for nonstationary and nonlinear energy price time series forecasting
B Zhu, X Shi, J Chevallier, P Wang… - Journal of …, 2016 - Wiley Online Library
For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive
multiscale ensemble learning paradigm incorporating ensemble empirical mode …
multiscale ensemble learning paradigm incorporating ensemble empirical mode …
Cooperative learning for radial basis function networks using particle swarm optimization
This paper presents a new evolutionary cooperative learning scheme, able to solve function
approximation and classification problems with improved accuracy and generalization …
approximation and classification problems with improved accuracy and generalization …
A step-by-step procedure for tests and assessment of the automatic operation of a powered roof support
D Szurgacz, S Zhironkin, M Cehlár, S Vöth, S Spearing… - Energies, 2021 - mdpi.com
A powered longwall mining system comprises three basic machines: a shearer, a scraper
(longwall) conveyor, and a powered roof support. The powered roof support as a component …
(longwall) conveyor, and a powered roof support. The powered roof support as a component …
Identification of shearer cutting patterns using vibration signals based on a least squares support vector machine with an improved fruit fly optimization algorithm
L Si, Z Wang, X Liu, C Tan, Z Liu, J Xu - Sensors, 2016 - mdpi.com
Shearers play an important role in fully mechanized coal mining face and accurately
identifying their cutting pattern is very helpful for improving the automation level of shearers …
identifying their cutting pattern is very helpful for improving the automation level of shearers …
[HTML][HTML] Quantum-enhanced least-square support vector machine: Simplified quantum algorithm and sparse solutions
Quantum algorithms can enhance machine learning in different aspects. Here, we study
quantum-enhanced least-square support vector machine (LS-SVM). Firstly, a novel quantum …
quantum-enhanced least-square support vector machine (LS-SVM). Firstly, a novel quantum …
Training soft margin support vector machines by simulated annealing: A dual approach
A theoretical advantage of support vector machines (SVM) is the empirical and structural risk
minimization which balances the complexity of the model against its success at fitting the …
minimization which balances the complexity of the model against its success at fitting the …
A study on regularized weighted least square support vector classifier
B Yang, Q Shao, L Pan, W Li - Pattern Recognition Letters, 2018 - Elsevier
Abstract Least Square Support Vector Machine (LSSVM) has been widely used for solving
regression and classification problems due to its simple solution. However, LSSVM has …
regression and classification problems due to its simple solution. However, LSSVM has …