基于即时学习的改进条件高斯回归软测量  

Improved conditional Gaussian regression soft sensor based on just-in-time learning

作　　者：黎宏陶 王振雷[1] 王昕[2] LI Hongtao;WANG Zhenlei;WANG Xin(Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China;Center of Electrical&Electronic Technology,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]华东理工大学能源化工过程智能制造教育部重点实验室,上海200237 [2]上海交通大学电工与电子技术中心,上海200240

出　　处：《化工学报》2024年第6期2299-2312,共14页CIESC Journal

基　　金：国家自然科学基金重大项目(62394345);国家自然科学基金面上项目(22178103,62373154);国家自然科学基金青年科学基金项目(62203173);中央高校基本科研业务费专项(222202417006)。

摘　　要：基于数据驱动的在线软测量是当前工业智能化感知的重要研究方向。在算法实际部署中,过程模态切换以及数据漂移都会导致软测量性能下降,传统自适应方法又存在模型单一、模态遗忘等不足。针对上述问题提出一种基于即时学习的样本时空加权条件高斯回归(STWCGR)软测量算法。该方法用概率密度估计和条件概率计算实现软测量建模和预测:首先根据即时学习思想通过样本时空混合加权方法筛选局部建模数据,然后结合高斯混合回归思想累积局部单高斯概率密度模型对数据分布进行拟合,最后引入预测动量更新和模态更新策略提高预测稳定性并赋予模型对新工况的学习适应能力。通过仿真实验验证了所提方法在预测精度、稳定性以及新模态适应能力上的有效性。Data-driven online soft sensing is an important research direction in current industrial intelligent sensing.In the practical use of algorithms,process mode switching and data drift might reduce the performance of soft sensors.Traditional adaptive approaches confront limitations,such as a limited variety of models and a tendency to forget previously acquired modes.A sample temporal and spatial weighted conditional gaussian regression(STWCGR)soft sensor algorithm based on just-in-time learning is proposed to overcome these issues.This algorithm uses probability density and conditional probability for soft sensing modeling and prediction.First,a sample spatiotemporal mixed-weight technique is used to pick local modeling data in accordance with the just-in-time learning principle.Then,the local Gaussian probability density models are accumulated to fit the data updates are introduced to enhance prediction stability and endow the model with adaptability to new working conditions. The efficacy of the suggested algorithm is confirmed by simulation studies with respect to forecast precision, stability, and flexibility to accommodate new modes.

关 键 词：智能感知 数据驱动软测量 预测 即时学习 高斯混合回归 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]