化工学报2024，Vol.75Issue(6)：2299-2312,14.DOI:10.11949/0438-1157.20240127
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
摘要
Abstract
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 distribution by incorporating the concept of Gaussian mixture regression.Finally,momentum updates and mode 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Key words
AI perception/data-driven soft sensor/prediction/just-in-time learning/Gaussian mixture regression分类
信息技术与安全科学引用本文复制引用
黎宏陶,王振雷,王昕..基于即时学习的改进条件高斯回归软测量[J].化工学报,2024,75(6):2299-2312,14.基金项目
国家自然科学基金重大项目(62394345) （62394345）
国家自然科学基金面上项目(22178103,62373154) （22178103,62373154）
国家自然科学基金青年科学基金项目(62203173) （62203173）
中央高校基本科研业务费专项(222202417006) （222202417006）
