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In this paper we demonstrate an improvement in the transient tracking performance when a Support Vector Regression (SVR) is used to identify a nonlinear ARMA plant. We use an on-line version of SVR, and at each instant, the identified model is used to determine the appropriate control law. A further improvement in the transient performance is shown with the methodology of multiple models, switching,...
Yield is a very important criterion to measure the semiconductor wafer fabrication facilities (FABs) productivity. The finished products will be check by Wafer Acceptance Test (WAT) and Circuit Probe (CP) to classified into ferior goods or inferior goods. This research applied the data from WAT and CP for the selection of the most important measuring parameters to improve the yield. Three methods,...
In order to make full use of the advantages of both parametric and non-parametric models simultaneously, a kind of semi-parametric support vector machine (SVM) was proposed by combining a non-parametric SVM model and a parametric linear basis function model. The semi-parametric SVM was used to estimate the Q values of continuous-state-discontinuous-action pairs in an on-line manner so as to generalize...
This paper explores application of support vector regression to adaptive inverse control problems. Support vector regression (SVR) has been proven to generate global solutions contrary to neural networks, because SVR basically solves quadratic programming (QP) problems. With this advantage, a plant model is identified and its inverse model is learned. In addition, adaptive algorithms for compensating...
There is an increasing interest in more accurate prediction of software maintainability in order to better manage and control software maintenance. Recently, TreeNet has been proposed as a novel advance in data mining that extends and improves the CART (classification and regression trees) model using stochastic gradient boosting. This paper empirically investigates whether the TreeNet model yields...
In this paper, we propose a novel nonlinear ensemble rainfall forecasting model integrating generalized linear regression with artificial neural networks (ANNs). In this model, using different linear regression extract linear characteristics of rainfall system. Then using different ANNs algorithms and different network architecture extract nonlinear characteristics of rainfall system. Thirdly, the...
Although supervised learning has been widely used to tackle problems of function approximation and regression estimation, prior knowledge fails to be incorporated into the data-driven approach because the form of input-output data pairs are not applied. To overcome this limitation, focusing on the fusion between rough fuzzy system and very rare samples of input-output pairs with noise, this paper...
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