关键词: 软测量, 多模型, 高斯过程回归, 证据理论, 仪表, 发酵, 算法

Abstract:

In this paper, a multi-model soft sensor method based on Dempster-Shafer theory (DS) and Gaussian process regression (GPR) was proposed. Firstly, GPR was used to build the sub-models of the proposed soft sensor after clustering training dataset. Secondly, the initial weightings were designed based on membership functions and output posteriori probabilities of GPR based sub-models, respectively. And the initial weightings were fused using the combination rule of DS. Finally, the weighted sum of sub-models with the fused weightings was used to output predictive means and uncertainty. The proposed method was validated on simulation data of a penicillin fermentation process and industrial data of an erythromycin fermentation process. For comparisons, single model-based soft sensor and traditional multi-model soft sensor were also studied. Simulations showed that the proposed method had better predictive accuracy and lower predictive uncertainty.

Key words: soft sensor, multi-model, Gaussian process regression, Dempster-Shafer theory, instrumentation, fermentation, algorithm