冷壁面霜层生长的支持向量机模型

针对结霜过程因具有明显的非线性特征,采用传统方法难以精确预测的问题。建立了基于支持向量机的冷壁面霜成生长的预测模型,应用实验数据对模型进行验证、评估,并与基于最小二乘法的非线性多元回归模型进行了对比、分析。结果表明,基于支持向量机的预测模型能够很好的解决非线性预测问题。在已建立的预测模型基础上,以霜层生长过程中传热率预测为例,分别在测试集中的自变量与因变量加入不同噪声信号对模型预测性能影响进行了研究。结果表明,基于支持向量机的模型具有良好的抗干扰能力。

Predication Model of Frost Growth on Cold Surface Based on Support Vector Machine

A novel machine learning method,support vector machine(SVM),was used to develop a prediction model for frost growth on cold surface.The prediction model was validated by experimental data.The values predicted were compared with those by multivariable nonlinear regression(NLR) model which is based on the least square method,and the results show that the SVM based model has a good prediction accuracy.Then the SVM based total heat flux prediction model was investigated as an example in the capability of dealing white noise signal by adding noise in the input vectors and the output vector of the training set.The result shows that the present SVM prediction models are robust against noise.