Automotive engine power and torque are significantly affected with effective tune-up. Current practice of engine tune-up relies on the experience of the automotive engineer. The engine tune-up is usually done by trial-and-error method, and then the vehicle engine is run on the dynamometer to show the actual engine output power and torque. Obviously the current practice costs a large amount of t...

The aim of this work was to analyze the variety of soil by laser-induced breakdown spectroscopy (LIBS) coupled with chemometrics methods. 6 certified reference materials (CRMs) of soil samples were selected and their LIBS spectra were captured. Characteristic emission lines of main elements were identified based on the LIBS curves and corresponding contents. From the identified emission lines, ...

In order to achieve continuous navigation capability in areas such as tunnels, urban canyons, and indoors a new approach using least squares support vector machine LS-SVM and H∞ filter HF for integration of INS/WSN is proposed. In the integrated system, HF estimates the errors of position and velocity while the signals in WSNs are available. Meanwhile, the compensation model is trained by LS-SV...

We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible sequential nature) of training data arising in reinforcement learning we employ a subspace based variant of LS-SVM that sequentially processes the data and is hence especially suited for online learning. This approach is adapte...

In this paper, an Optimally Pruned Extreme Learning Machine (OP-ELM) is applied to the problem of long-term time series prediction. Three known strategies for the long-term time series prediction i.e. Recursive, Direct and DirRec are considered in combination with OP-ELM and compared with a baseline linear least squares model and Least-Squares Support Vector Machines (LS-SVM). Among these three...

Abstract Drought stress under a changing climate can significantly affect agricultural production. Simulation of soil water dynamics in field conditions becomes necessary to understand changes develop irrigation guidelines. In this study, three models including Auto-Regressive Integrated Moving Average (ARIMA), Back-Propagation Artificial Neural Network (BP-ANN), and Least Squares Support Vecto...

Quantum machine learning methods have the potential to facilitate using extremely large datasets. While availability of data for training models is steadily increasing, oftentimes it much easier collect feature vectors obtain corresponding labels. One approaches addressing this issue use semi-supervised learning, which leverages not only labeled samples, but also unlabeled vectors. Here, we pre...

Exponential increase in power consumption leads to the global attention towards pollution free and renewable energy resources. For instance, wind turbines to produce electrical energy thru wind energy. For wind energy domain, wind speed forecasting is of great significance for wind farms design and planning, its operational control, and wind power prediction etc. Due to the impact of several en...

Least Squares Support Vector Machines (LS-SVM) are the state of the art in kernel methods for regression and function approximation. In the last few years, these models have been successfully applied to time series modelling and prediction. A key issue for the good performance of a LS-SVM model are the values chosen for both the kernel parameters and its hyperparameters in order to avoid overfi...