Application of smooth support vector regression in flame combustion state prediction

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.