In this paper a method based on kernel principal component analysis (KPCA) and mixed kernel least square support vector machine regression (MKLS-SVM) for online quality prediction in atmospheric distillation column is presented. Firstly, the KPCA is employed to reduce the input vector’s dimensions of the multiple-input multiple-output (MIMO) soft sensor and created the data set which required t...

In this contribution, we propose a robust highly selective nonlinear channel estimator for Single -Input Multiple-Output (SIMO) Orthogonal Frequency Division Multiplexing (OFDM) system using complex Least Squares Support Vector Machines (LS-SVM) and applied to Long Term Evolution (LTE) downlink under high mobility conditions . The new method uses the information provided by the pilot signals to...

This paper presents the application of least squares support vector machines (LS-SVMs) for automatic detection of alterations in the human electroencephalogram (EEG) activities during hypopnoea episodes. The obstructive sleep apnoea hypopnoea syndrome (OSAH) means ‘‘cessation of breath” during the sleep hours and the sufferers often experience related changes in the electrical activity of the b...

Moisture content (MC) is one of the important indexes to evaluate maize seed quality. Its accurate prediction very challenging. In this study, long-wave near-infrared hyperspectral imaging (LW-NIR-HSI) system was used, and embryo side (S1) endosperm (S2) spectra each were extracted, as well average spectrum (S3) both being calculated. The partial least square regression (PLSR) least-squares sup...

This paper studies a method to obtain sparseness and structure detection for a class of kernel machines related to Least Squares Support Vector Machines (LS-SVMs). The key method to derive such kernel machines is to adopt an hierarchical modeling strategy. Here, the first level consists of an LS-SVM substrate which is based upon an LS-SVM formulation with additive regularization trade-off. This...

Usually time series prediction is done with regularly sampled data. In practice, however, the data available may be irregularly sampled. In this case the conventional prediction methods cannot be used. One solution is to use Functional Data Analysis (FDA). In FDA an interpolating function is fitted to the data and the fitting coefficients are being analyzed instead of the original data points. ...

In this paper, a classifier is proposed and trained to distinguish between bulking and non-bulking situations in an activated sludge wastewater treatment plant, based on available image analysis information and with the goal of predicting and monitoring filamentous bulking. After selecting appropriate activated sludge parameters (filament length, floc fractal dimension and floc roundness), an L...

We present a subspace-based variant of LS-SVMs (i.e. regularization networks) that sequentially processes the data and is hence especially suited for online learning tasks. The algorithm works by selecting from the data set a small subset of basis functions that is subsequently used to approximate the full kernel on arbitrary points. This subset is identified online from the data stream. We imp...

This paper deals with the problem of multi-class classification in machine learning. Various techniques have been successfully proposed to solve such problems, with a computation cost often much higher than techniques dedicated to binary classification. To address this problem, we propose a novel formulation for designing multi-class classifiers, with essentially the same computational complexi...