Design of adaptive soft sensor based on Bayesian optimization
Design of adaptive soft sensor based on Bayesian optimization
复制标题
DOI：
10.1016/j.cscee.2022.100237
复制
发表时间：
2022-07
影响因子：
--
通讯作者：
Shuto Yamakage;H. Kaneko
中科院分区：
文献类型：
--
作者：
Shuto Yamakage;H. Kaneko
当自适应软测量被引入到工业设备中时，对于预测软测量，必须选择自适应机制的类型、机制的超参数、回归模型和模型的超参数的适当组合。提出了一种基于贝叶斯优化的自适应软测量自动选择方法。建立了自适应软测量候选值与其预测能力之间的高斯过程回归模型，进行贝叶斯优化。选择具有最大捕获值函数的自适应软测量候选。通过对两个真实的工业数据集的分析，验证了该方法的有效性。
When adaptive soft sensors are introduced to industrial plants, an appropriate combination of the type of adaptation mechanism, hyperparameters of the mechanism, regression model, and hyperparameters of the model must be selected for predictive soft sensors. We propose an automatic and efficient selection method for adaptive soft sensors based on Bayesian optimization. A Gaussian process regression model was constructed between the candidates of adaptive soft sensors and their predictive ability to perform Bayesian optimization. The adaptive soft-sensor candidate with the maximum acquisition value function was selected. The effectiveness of the proposed method was confirmed by analyzing two real industrial datasets.