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In order to improve the credibility of LSSVM based soft-sensing model, mean value of the fitting error, mean value of the prediction error and the biggest prediction error are suggested as the objectives for model parameters optimization. The preference for the best parameters selection is set according to the difference between the biggest prediction error and the mean value of prediction error. Simulation results of oxygen content in flue gas of a 600 MW supercritical unit in a coal-fired power plant verify the rationality of the preference. By selecting the parameter γ and σ based on the preference, the fitting ability and the predicting ability are considered simultaneously, and the biggest prediction error will not exceed the upper bound, thus the reliability of the model is enhanced. On the basis of this, the further simulation of changing the parameter proves that considering the whole three objectives for model parameters selection is reasonable.