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In this paper, a novel nonlinear Radial Basis Function Neural Network (RBF-NN) ensemble model based on ν-Support Vector Machine (SVM) regression is presented for financial time series forecasting. In the process of ensemble modeling, the first stage the initial data set is divided into different training sets by used Bagging and Boosting technology. In the second stage, these training sets are input...
Least squares support vector regression is presented in gross industrial output value prediction in the paper. Least squares support vector regression is a kind modified support vector regression. It can solve a convex quadratic programming problem, which has higher performance than support vector regression. The data of gross industrial output value in Fujian province from 1990 to 2006 are employed...
We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach for constructing radial basis function (RBF) classifiers with tunable nodes. At each stage of the OFR construction process, the centre vector and diagonal covariance matrix of one RBF node is determined efficiently by minimising the leave-one-out (LOO) misclassification rate (MR) using a PSO algorithm...
Detection Principle of carrier catalytic methane sensor is introduced and the nonlinear problem of the sensor is indicated. In order to enhance the measure precision of the methane sensor, the nonlinear compensation model was set up by adopting Least Squares Support Vector Regression which is an Support Vector Machines version that works with a least squares cost function, Support Vector Machines...
This paper deals with the application of least squares support vector regression (LS-SVR) with radial basis function (RBF) kernel in dam crack forecasting. In the process of LS-SVR, we performed the standard grid search and particle swarm optimization (PSO) to tune hyperparameters of LS-SVR. The results demonstrate that our PSO approach can identify optimal or near optimal parameters faster than the...
Equal Salt Deposit Density (ESDD) is a main factor to classify contamination severity and draw pollution distribution map. To cope with the problems existing in the ESDD predicting by multivariate linear regression (MLR), back propagation (BP) neural network and least squares support vector machines (LSSVM), a nonlinear combination forecasting model based on wavelet neural network (WNN) for ESDD is...
This paper introduces a novel customers' demand forecasting model based on least squares support vector machines (LS-SVM) for e-business enterprises. Firstly, the paper presents actual state of e-business, and discusses some factors that block e-business advance in China. Then, some common techniques used for forecasting are briefly reviewed together with their shortcomings respectively. To solve...
Dry point of aviation kerosene in the atmospheric distillation column is a very important process value for quality controlling. But unfortunately few on-line hardware sensors are available to this value or such sensors are difficult to maintain. This paper adopts a novel method based on least squares support vector machine (LS-SVM) regression to implement on-line estimation of aviation kerosene dry...
By combining the basic idea of weighted least squares support vector machines (WLS-SVM) and fuzzy support vector machines (FSVM), a weight-setting strategy based on 2-norm distance and neighborhood density (WLS-SVM I) is presented in this paper. Then the relationship between the 2-norm distance and RBF kernel is revealed. Consequently, an equivalent weight setting strategy (WLS-SVM II) using information...
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