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Reducing power consumption has become a priority in microprocessor design as more devices become mobile and as the density and speed of components lead to power dissipation issues. Power allocation strategies for individual components within a chip are being researched to determine optimal configurations to balance power and performance. Modelling and estimation tools are necessary in order to understand...
Using microfossil-based transfer functions, domain scientists from the field of pale oceanography seek to reconstruct environmental conditions at various times in the past. This is accomplished by first determining a quantitative relationship between a forcing function, such as temperature, and the modern for aminiferal response using a calibration data set based on environmental data from an oceanographic...
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,...
Support vector machine (SVM) is a new method based on statistical learning theory. It has been successfully used to solve nonlinear regression and time series problems. However, SVM has rarely been applied to software reliability prediction. In this study, an SVM-based model for software reliability forecasting is proposed. In addition, the parameters of SVM are determined by Genetic Algorithm (GA)...
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...
We propose a hybrid approach of support vector regression, genetic algorithm, and seasonal moving window to explore seasonality effect for the stock indexes in three developed and one emerging markets using daily prices from 1996 to 2005. First, we utilize genetic algorithm to locate the approximate optimal combination of technical indicators. Then the property of nonlinearity and high dimensionality...
Forecasting of runway incursion events is very significant to guide the job of civil aviation safety management and it is an important part of the runway incursion early warning management. However, forecasting of runway incursion events is a complicated problem due to its non-linearity and the small quantity of training data. As a novel type of learning machine, support vector machine has some merits,...
Support vector regression (SVR) is one of the new methods of soft sensor modeling for estimating the products of metabolism in microorganism fermentations. The accuracy of SVR is mainly impacted by two factors: input variables selection and parameters set in SVR training procedures. But it is difficult to select the input variables and set the parameters. A novel method of soft sensor modeling is...
Support vector regression optimized by genetic algorithm (G-SVR) is proposed to forecast tourism demand. Genetic algorithm (GA) is used to search for SVR's optimal parameters, and adopt the optimal parameters to construct the SVR models. This study examines the feasibility of SVR in tourism demand forecasting by comparing it with back-propagation neural networks (BPNN).The experimental results indicate...
Forecasting the tax gross exactly is significant to carry on the macroscopic regulation efficiently under the market economy. Conventional linear macroscopic economic model is very difficult to hold non-linear phenomena in economic system, thus the tax forecasting error will increase. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small...
Freight volume forecasting is significant to highway web plan. Here, support vector regression optimized by genetic algorithm (G-SVR) is proposed to forecast freight volume. We adopt genetic algorithm (GA) to seek the optimal parameters of SVR in order to improve the efficiency of prediction. The data of freight volume in a certain port from 1998 to 2007 is used as a case study. The experimental results...
Aiming at addressing the optimization design problems with implicit objective performance functions, a genetic optimization design methodology based on the support vector regression (SVR) response surface is proposed. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the experiments or numerical simulations. Applying...
An accurate friction model is necessary for friction compensation in radar servo systems or industrial robots. In order to obtain an accurate friction model, a method of friction modelling is proposed, based on support vector regression machines (SVRM) and real genetic algorithms (RGA). Three optimization problem formulations are proposed to realize the automatic optimal parameter selection of SVMR...
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