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

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