Abstract:

A new online modeling method based on adaptive local learning and least squares support vector regression was proposed for nonlinear multi-input multi-output processes.Both distance measure and angle measure were used to evaluate the similarity between data, thus a more comprehensive and relevant set was constructed.In the present method, the model parameters were optimized online by using the fast leave-one-out criterion.In addition, an online adaptive model selection strategy for modeling of fermentation processes was developed.Simulations on a fed-batch streptokinase fermentation process showed that the active biomass and streptokinase concentrations could both be predicted simultaneously just from the second batch.The results also showed that the proposed method was more accurate and adaptive compared with the alternative modeling methods.

Key words:

自适应局部学习, 最小二乘支持向量机回归, 快速留一法, 在线建模, 发酵过程

摘要：

提出一种基于自适应局部学习的最小二乘支持向量机回归（LSSVR）在线建模方法。考虑样本间的距离和角度信息以获得更全面合理的相似样本集，推导了采用快速留一法在线优化模型参数的准则，并给出了发酵过程在线自适应模型选择的策略。以链激酶流加发酵过程为例，验证了所提出算法能够从过程的第2批次开始，同时对活性菌体浓度和链激酶浓度进行较准确的在线预报，较普通的局部LSSVR等建模方法具有更高的预报精度和自适应性。

关键词:

自适应局部学习, 最小二乘支持向量机回归, 快速留一法, 在线建模, 发酵过程