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...

This paper presents the Optimally-Pruned Extreme Learning Machine (OP-ELM) toolbox. This novel, fast and accurate methodology is applied to several regression and classification problems. The results are compared with widely known Multilayer Perceptron (MLP) and Least-Squares Support Vector Machine (LS-SVM) methods. As the experiments (regression and classification) demonstrate, the OP-ELM meth...

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...

Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs). In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO-) type decomposition methods is proposed. By the new method, we can select a single direction to achieve the convergence of the optimality condition. A simple asymptot...

Least-Squares Support Vector Machines (LS-SVM’s), originating from Stochastic Learning theory, represent a promising approach to identify nonlinear systems via nonparametric estimation of nonlinearities in a computationally and stochastically attractive way. However, application of LS-SVM’s in the identification context is formulated as a linear regression aiming at the minimization of the l2 l...

As a key hydrological parameter, daily reference evapotranspiration (ETo) determines the accuracy of the hydrological number of the crop, and, consequently, the regional optimization disposition of water resources. At present, the main methods for ETo estimation are the Penman-Monteith (PM) equation and its modified formula, both of which are based on climatic factors such as temperature, radia...