基于多相似度局部状态辨识的集成学习自适应软测量方法

Process industry are usually characterized by complex process characteristics such as nonlinearity, multiplicity of phases and modes, and time-varying behavior, which leads to poor prediction performance for traditional global and ensemble soft sensors. Thus, an adaptive soft sensor modeling method named Multi-Similarity based Online Selective Ensemble (MSOSE) was proposed based on ensemble learning with multi-similarity local state identification. Its implementation included three main stages. In the offline modeling stage, the local process states were i-dentified by using different similarity criteria, and then a set of diverse local models was built. In the online prediction stage, the online dynamic selection of local models, the adaptive determination of model weights and the fusion of local prediction results were achieved through a two-level ensemble strategy. In the update phase, Kullback-Eeibler (KE) divergence was used to evaluate the difference between the current and the adjacent state data distributions to achieve real-time detection of concept drift, and then decide whether to add a local model online or not. Moreover, the obtained offline analysis data were added to the modeling database. The effectiveness and superiority of MSOSE were verified through an industrial chlortetracycline fermentation process and an industrial debutanizer process.