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...

This paper describes the pattern recognition technique based on multiscale discrete wavelet transform(MDWT) and least square support vector machine (LS-SVM) for the classification of EEG signals. The different statistical features are extracted from each EEG signal corresponding to various seizer and nonsiezer brain functions, using MDWT. Further these sets of features are fed to the LS-SVM mul...

It has been shown that Kernel Based Regression (KBR) with a least squares loss has some undesirable properties from robustness point of view. KBR with more robust loss functions, e.g. Huber or logistic losses, often give rise to more complicated computations. In this work the practical consequences of this sensitivity are explained, including the breakdown of Support Vector Machines (SVM) and w...

In this paper, variable selection and variable scaling are used in order to select the best regressor for the problem of time series prediction. Direct prediction methodology is used instead of the classic recursive methodology. Least Squares Support Vector Machines (LS-SVM) are used in order to avoid local minimal in the training phase of the model. The global methodology is applied to the tim...

This paper presents a simple estimate to determine the width of Gaussian kernel with the adaptive scaling technique. The Gaussian kernel is widely employed in Radial Basis Function (RBF) network, Support Vector Machine (SVM), Least Squares Support Vector Machine (LS-SVM), Kriging, and so on. It is widely known that the width of the Gaussian kernel in these machine learning techniques plays an i...

This study presents a least squares support vector machine (LSSVM) based displacement prediction model for health monitoring of concrete dams. LSSVM is a novel machine learning technique. The model can produce similar good generalization performance and learns faster than the basic support vector machines in engineering problems. The advantages such as high prediction accuracy, fast training sp...

integrally skinned asymmetric membranes based on nanocompositepolyethersulfone were prepared by the phase separation process using the supercritical co2 as a nonsolvent for the polymer solution. in present study, the effects of temperature and nanoparticle on selectivity performance and permeability of gases has beeninvestigated. it is shown that the presence of silica nanoparticles not only di...

This paper proposes the use of least-squares support vector machines (LS-SVMs) as a relatively new nonlinear multivariate calibration method, capable of dealing with ill-posed problems. LS-SVMs are an extension of "traditional" SVMs that have been introduced recently in the field of chemistry and chemometrics. The advantages of SVM-based methods over many other methods are that these lead to gl...