NDLI: Application of Smooth Support Vector Regression in Flame Combustion State Prediction

Content Provider	IEEE Xplore Digital Library
Author	Xin Zhang Bing Wang Pu Zhao Chao Zhang
Copyright Year	2007
Description	Author affiliation: Hebei Univ., Baoding (Xin Zhang)
Abstract	Time series analysis and prediction is an important means of dynamic system modeling. A new method of time series prediction based on support vector regression (SVR) is introduced to resolve the problem of non-linear system modeling. For the purpose of reducing calculation complexity, smooth method is presented to improve standard SVR arithmetic, and is utilized to build the combustion state model of flame in the furnace of utility boilers according to the feature parameters of flame image, in order to predict the combustion state of flame. The flame images are gained from the flame image gathering system on-line. The feature parameters of the flame image are extracted, and are used to determined combustion indices which can denote different combustion states of flame. The time series of combustion indices are used for constructing the smooth support vector regression (SSVR) model and predicting the combustion state of flame. The results of experimentation indicate that SSVR has excellent performance on time series prediction. Compared with traditional time series prediction method such as artificial neural network, SSVR has faster convergence speed and higher fitting precision, which effectively extends the application of SVR.
Starting Page	2901
Ending Page	2906
File Size	613779
Page Count	6
File Format	PDF
ISBN	9781424409723
DOI	10.1109/ICMLC.2007.4370643
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fires Combustion Modeling Predictive models Time series analysis Nonlinear dynamical systems Arithmetic Furnaces Boilers Prediction methods Flame image Support vector regression Smooth method Time series prediction Combustion state of flame
Content Type	Text
Resource Type	Article

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