基于自适应模糊支持向量机的L-赖氨酸发酵过程软测量方法研究
首发时间:2011-01-26
摘要:针对生化反应过程中软测量模型随着时间的推移而出现的模型老化现象,提出一种基于增量学习的自适应模糊支持向量机软测量建模方法。它首先将输入空间中的样本映射到高维特征空间,然后根据样本偏离超平面的程度赋予不同的模糊隶属度,建立模糊支持向量机软测量模型,并在模型投入现场运行后,通过一种改进的增量学习算法在线更新模型参数以自适应获得更加准确的软测量模型。以L-赖氨酸流加发酵过程为例,验证了所提算法能够从过程的第2批次开始对关键生物量参数(菌丝浓度和基质浓度)进行较准确的在线预测,较普通的模糊支持向量机建模方法具有较高的预测精度和自适应性。
关键词: 自适应学习 模糊支持向量机 软测量 L-赖氨酸发酵
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Research on Soft Sensing Method Based on Adaptive Fuzzy Support Vector Machines for L-lysine Fermentation Processes
Abstract:In order to overcome the emergence of aging model as times move on, a soft sensing modeling method based on Incremental Learning and fuzzy support vector machines is presented. Data samples in input space are mapped into high dimensional feature space. The fuzzy membership value to each input point is computed according to the distances to the hyperplane, and the soft sensing model based on fuzzy support vector machines is established. In addition, after the model is turned into application, the model can be update on-line through improved incremental learning algorithm. Simulations on a fed-batch L-lysine fermentation process shows that the crucial biological parameters can be predicted from the second batch. The results also show that proposed method is more accurate and adaptive compared with the alternative modeling methods.
Keywords: adaptive learning fuzzy support vector machines soft sensing L-lysine fermentation process
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