关键词: 最小二乘支持向量机, 蜡沉积速率, 预测, 模型, 模型精度

Abstract: Considering the complexity of the influence factors of wax deposition and the advantage of least squares support vector machine in small sample prediction，based on the prediction principles of least squares support vector machine，by optimizing the parameters of least squares support vector machine，the prediction model of wax deposition rate was established and wax deposition rate was predicted. The method had higher accuracy when the samples were fewer，and the prediction results of wax deposition rate was in good agreement with the experimental data. The least squares support vector machine could get the intuitive function expression when it was used to establish the model of wax deposition rate，while neural network method could not get explicit expression. so this method has sufficient preponderance. When the RBF kernel function was used，different initial values of regularization parameters ? and kernel bandwidth ? had a greater impact on the predicted results，so it should be used with care.

Key words: least squares support vector machine, wax deposition rate, prediction, model, model accuracy