Research on Application of Sintering Basicity of Based on Various Intelligent Algorithms

Prediction of alkalinity in sintering process is difficult. Whether the level of the alkalinity of sintering process is successful or not is directly related to the quality of sinter. There is no good method, prediction of alkalinity by high complexity, the present nonlinear, strong coupling, high time delay, so the recent new technology, grey least square support vector machine have been introduced. In this paper, The weight of evaluation objectives has not given the corresponding consideration when solving the correlation degree by taking traditional grey relation analysis and it is with a lot of subjective factors, easily lead to mistakes in decision-making on program. What is more a kind of alkaline grey support vector machine model, enables us to develop new formulations and algorithms to predict the alkalinity. In the model, the data sequence of fluctuation is composed of grey theory and support vector machine is weakened, can deal with nonlinear adaptive information, combination and grey support vector machine these advantages. The results show that, the basicity of sinter, can accurately predict the small sample and reference information using the model. The experimental results show that, the grey support vector machine model is effective and with practical advantages of high precision, less samples, and simple calculation.