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Melt index is considered one of the most important variables in determining chemical product quality and thus reliable prediction of melt index (MI) is essential in practical propylene polymerization processes. In this paper, a fuzzy support vector regression (FSVR) based model for propylene polymerization process is developed to predict the MI of polypropylene from other easily measured process variables...
Particle size is the key technical index of grinding process, but it is difficult to be measured online directly, and the lab analysis has large-time delay. Combined with the advantage of soft-sensor modeling based on support vector regression (SVR), a soft-sensor approach for particle size of grinding process with SVR is proposed. Firstly, the empirical formulae are used to roughly determine the...
Long-term business cycle forecasting is a very important issue in economic evaluation. This study presents a novel intuitionistic fuzzy least-squares support vector regression (IFLS-SVR) model for accurately forecasting long-term index of business cycle. Traditional support vector regression (SVR) and least-squares support vector regressions (LS-SVR) have been successfully applied in forecasting problems...
This paper proposes an intelligent trading system using support vector regression optimized by genetic algorithms (SVR-GA) and multilayer perceptron optimized with GA (MLP-GA). Experimental results show that both approaches outperform conventional trading systems without prediction and a recent fuzzy trading system in terms of final equity and maximum drawdown for Hong Kong Hang Seng stock index.
Stock index prediction seems to be a challenging task of the financial time series prediction process especially in emerging markets with their complex and inefficient structures. Multivariate adaptive regression splines (MARS) is a nonlinear and non-parametric regression methodology and has been successfully used in classification tasks. However, there are few applications using MARS in stock index...
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