In this paper, a new robust approach based on Least Square Support Vector Machine (LSSVM) as a proxy model is used for an automatic fractured reservoir history matching. The proxy model is made to model the history match objective function (mismatch values) based on the history data of the field. This model is then used to minimize the objective function through Particle Swarm Optimization (...

This paper presents a new method for the selection of the two hyperparameters of Least Squares Support Vector Machine (LS-SVM) approximators with Gaussian Kernels. The two hyperparameters are the width σ of the Gaussian kernels and the regularization parameter λ. For different values of σ, a Nonparametric Noise Estimator (NNE) is introduced to estimate the variance of the noise on the outputs. ...

This paper is concerned with kernel-based techniques for automated categorization of laryngeal colour image sequences obtained by video laryngostroboscopy. Features used to characterize a laryngeal image are given by the kernel principal components computed using the N -vector of the 3-D colour histogram. The least squares support vector machine (LS-SVM) is designed for categorizing an image se...

This paper presents a multiclass, multilabel implementation of Least Squares Support Vector Machines (LS-SVM) for direction of arrival (DOA) estimation in a CDMA system. For any estimation or classification system the algorithm’s capabilities and performance must be evaluated. Specifically, for classification algorithms a high confidence level must exist along with a technique to automatically ...

In Functional Data Analysis (FDA) multivariate data are considered as sampled functions. We propose a non-supervised method for finding a good function basis that is built on the data set. The basis consists of a set of Gaussian kernels that are optimized for an accurate fitting. The proposed methodology is experimented with two spectrometric data sets. The obtained weights are further scaled u...

This paper discusses a numerical method based on Least Squares Support Vector Machines (LS-SVMs) for solving linear time varying initial and boundary value problems in Differential Algebraic Equations (DAEs). The method generates a closed form (model-based) approximate solution. The results of numerical experiments on different systems with index from 0 to 3, are presented and compared with ana...

In order to improve the prediction accuracy of busy telephone traffic which is influenced by multiple factors, this paper proposes a combined forecasting model which takes the influence of multiple factors into consideration and combines three models ——wavelet transform, autoregressive integrated moving average (ARIMA) model and least squares support vector machines (LSSVM) model, LSSVM is opti...

The present study proposes a low-energy consumption multipoint sampler carried by deep-sea landing vehicle (DSLV) to meet the requirements of time series sampling in local areas and location wide areas, an optimization method structure based on least-squares support-vector machine (LSSVM) surrogate model multi-objective particle swarm (MOPSO) algorithm. First, overall core components, such as s...

Least squares twin support vector machine (LS-TSVM) aims at resolving a pair of smaller-sized quadratic programming problems (QPPs) instead of a single large one as in the conventional least squares support vector machine (LS-SVM), which makes the learning speed of LS-TSVM faster than that of LS-SVM. However, same penalties are given to the negative samples when constructing the hyper-plane for...