摘要
针对间歇生产过程中,采集的数据存在非高斯、非线性的特征,本文将支持向量数据描述(Support Vector Data Description,SVDD)的方法应用到间歇过程故障监测中。首先,将数据按照批次展开并进行标准化,再按照变量展开;然后,建立SVDD模型,应用核函数求出模型半径R(?)对新的待检测样本,先计算其与模型中心的距离,再与半径比较,判断它是否正常。因为SVDD可以利用核函数替代向量内积的计算,所以能够解决非高斯、非线性数据的检测问题。最后,在青霉素发酵过程监测的成功应用,验证了该方法的有效性、准确性。
For batch production process, non-gaussian and nonlinear characteristics also exist dataset. Support vector data description （SVDD） method is used in this paper. Firstly, the dataset is first unfolded though the batch and standardization should also be performed nextly, then it is re-unfold through the variable direction. After that, a SVDD model can be built and kernel function is applied to solve the radius of the model, for the new samples to be detected, calculating the distance to the model center, comparing with the radius and then which can be determined whether it is normal. Because the SVDD could use kernel function instead of inner product of vector computation, it can solve the detection problem of nonlinear and non-gaussian data. Finally, in the monitoring of the successful application of penicillin fermentation process, SVDD is verified to be effective and accurate.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2013年第12期1401-1405,共5页
Computers and Applied Chemistry
基金
国家自然科学基金重点项目(61034006)
国家自然科学基金项目(61174119)
