基于多模态扰动的集成即时学习软测量建模

Soft Sensor Development Based on Ensemble Just-in-time Learning with Multimodal Perturbation

近年来,即时学习软测量方法已被广泛用于过程工业中难测参数的在线估计.然而,常规的即时学习软测量方法仅依靠单一的学习配置,忽略了即时学习性能的多样性,导致预测性能不佳.为此,提出了一种基于多模态扰动的集成即时学习软测量建模方法,称为DSS-ELWPLS(Diverse Subspaces and Similarity measures based Ensemble Locally Weighted Partial Least Squares).该方法以局部加权偏最小二乘算法(LWPLS)为基学习器,通过输入特征扰动和相似度扰动以激发即时学习的多样性,然后基于进化多目标优化构建满足多样性和准确性的即时学习基模型.随后采用Stacking集成学习策略,实现即时学习基模型的融合.通过在青霉素发酵过程和工业混炼胶过程中的应用,验证了DSS-ELWPLS方法的有效性和优越性.

Recently,just-in-time learning(JIT)based soft sensors have been widely used to provide online estimates of difficult-to-measure variables in process industry.However,conventional JIT soft sensors rely only on a single learning configuration while ignoring the diversity of JIT learning,leading to poor prediction performance.To address this issue,we propose a multimodal perturbation-based ensemble JIT learning soft sensing algorithm,referred to as diverse subspaces,and similarity measures based on ensemble locally weighted partial least squares(DSS-ELWPLS).This method achieves diversity creation of JIT learning by exploiting input feature and similarity perturbations.Then,we generate a set of base JIT learners that are accurate but diverse through evolutional multi-objective optimization,with locally weighted partial least squares as the base learner.Finally,we combine all base JIT learners using the stacking strategy.The effectiveness and superiority of the proposed DSS-ELWPLS soft sensor method are demonstrated through its application to the penicillin fermentation and industrial rubber mixing processes.