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In order to study the application validity and veracity of regression technique of LS-SVM (Least Squares Support Vector Machine)in damage identification for plate structure, a numerical simulation of plate structure enduring great impact loading is built. Through the numerical simulation, the training samples are get for building the regression model of LS-SVM. Then the simulation experiments are...
A novel support vector regression (SVR) optimized by an integrated particle swarm optimization (PSO) was proposed. The optimization mechanism combined the discrete-valued PSO with the continuous-valued PSO to optimize the input feature subset selection and the SVR kernel parameter setting. By incorporating two types of PSO, the parameters and the input features of SVR were optimized simultaneously...
At present, the load modeling is still one of the difficult problems in power system. In this paper, a new method is presented which uses wide-area measurements and support vector machine (SVM) for load modeling. Based on wide-area measurements, this method does non-linear regression analysis on model with SVM. By using radial basis function (RBF), model structure is optimized, and Bayesian Framework...
This paper introduces a general Bayesian framework for obtaining sparse solutions to regression predicting, and the practical model 'relevance vector machine' (RVM) by Michael E. Tipping. As a brand-new thought of probabilistic learning model, it offers the superior level of generalization accuracy and a number of additional advantages comparable with the popular and state-of-the-art 'support vector...
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