基于支持向量机与遗传算法的灰熔点预测

为了提高估算煤灰熔点的精度，文中采用支持向量机算法对求解灰熔点问题进行了建模，并利用遗传算法对支持向量机模型的参数进行了优化，获得了最优的模型参数。支持向量机模型将灰成分作为输入量，煤的灰熔点Tst作为输出量，用试验数据对模型进行了校验和参数的寻优，利用优化后的模型对单煤和混煤灰熔点进行了预测，并将预测结果与实验结果进行了对比，结果表明，优化后的支持向量机模型实现了对单煤和混煤灰熔点较精确的预测。支持向量机可用于小样本问题的学习，计算速度快，提高了实时处理与预测能力。

Combining Support Vector Machine and Genetic Algorithm to Predict Ash Fusion Temperature

In order to improve the accuracy of predicting performance for ash fusion temperature,a support vector machine(SVM) model is employed,and parameters of the SVM model optimized by genetic arithmetic(GA) and best parameters were obtained.The compositions of coal ash were employed as inputs,and the ash fusion temperature was used as output of the SVM model.The model was verified with the experiment datum,result of prediction by the optimized SVM model was compared with the test datum,and the result show the SVM model has achieved good predicting performance for both single coals and blend coals.The SVM can be used with small sample size,and has higher calculation speed,and the ability of prediction on-line is improved.