基于支持向量回归机的复杂产品费用估算研究

Research of complex product cost estimation based on support vector regression

传统的费用估算算法需要大量的样本数据来保证其估算的准确性,但在实际应用中,由于样本数据的有限性,其准确性无法得到保证,针对这种情况提出使用基于统计学习理论的支持向量回归机(SVR)进行费用估算,并通过具体实例详细阐述基于SVR的费用估算具体步骤,包括数据预处理、基于SVR的训练、估算和后处理过程,通过与神经网络方法相比,实验结果验证了SVR在小样本情况下具有更好的估算精度。最后实现了基于SVR的复杂产品费用估算方法,并集成于复杂产品费用估算系统。

Since plenty of sample data is required to ensure the accuracy of traditional cost estimation algorithm,and it is hard to ensure the accuracy of estimation due to the limitation of sample data in practical application,the support vector regres-sion（SVR）based on statistical learning theory is proposed to make cost estimation. The specific steps of cost estimation is de-scribed in detail based on SVR,including data preprocessing,training based on SVR,estimation and post-processing. The ex-periment result verifies that the estimation accuracy based on SVR in small sample data is better than the method of neural net-work. Finally,the method of complex product cost estimation based on SVR is implemented,and is integrated in the system of complex product cost estimation.