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...

Frequency estimation is transformed to a pattern recognition problem, and a least squares support vector machine (LS-SVM) estimator is derived. The estimator can work efficiently without the need of statistics knowledge of the observations, and the estimation performance is insensitive to the carrier phase. Simulation results are presented showing that proposed estimators offer better performan...

Aiming at the problem that the three-phase APF’s dynamic model is a multi-variable, nonlinear and strong coupling system, an internal model controller for three-phase APF based on LS-Extreme Learning Machine is studied in this paper. As a novel single hidden layer feed-forward neural networks, extreme learning machine (ELM) has several advantages: simple net structural, fast learning speed, goo...

The accuracy of traditional monitoring methods using remote sensing was lower, because of the limited number of monitoring points on the Tai lake. This paper proposed to use the Least Squares Support Vector Machine (LS-SVM) theory to improve the accuracy of water quality retrieval, which is suitable for the small-sample fitting. The LS-SVM model was used to monitor concentration of suspended ma...

The least squares support vector machine (LS-SVM) has shown to exhibit excellent classification performance in many applications. In this paper, we propose an incremental and decremental LS-SVM based on updating and downdating the QR decomposition. It can efficiently compute the updated solution when data points are appended or removed. The experiment results illustrated that the proposed incre...

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...

Model-free estimates of the noise variance are important for doing model selection and setting tuning parameters. In this paper a data representation is discussed which leads to such an estimator suitable for multidimensional input data. The visual representation called the differogram cloud is based on the 2-norm of the differences of the inputand output-data. A corrected way to estimate the v...

This paper studies a method for the identification of Hammerstein models based on Least Squares Support Vector Machines (LS-SVMs). The technique allows for the determination of the memoryless static nonlinearity as well as the estimation of the model parameters of the dynamic ARX part. The SISO as well as the MIMO identification cases are elaborated. The technique can lead to significant improv...

Control valve is a kind of essential terminal control component which is hard to model by traditional methodologies because of its complexity and nonlinearity. This paper proposes a new modeling method for the upstream pressure of control valve using the least squares support vector machine (LS-SVM), which has been successfully used to identify nonlinear system. In order to improve the modeling...