[1]祁成,史旭东,熊伟丽.基于二阶相似度的即时学习软测量建模方法[J].智能系统学报,2020,15(5):910-918.[doi:10.11992/tis.201809040]
 QI Cheng,SHI Xudong,XIONG Weili.A just-in-time learning soft sensor modeling method based on the second-order similarity[J].CAAI Transactions on Intelligent Systems,2020,15(5):910-918.[doi:10.11992/tis.201809040]
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基于二阶相似度的即时学习软测量建模方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年5期
页码:
910-918
栏目:
学术论文—机器学习
出版日期:
2020-09-05

文章信息/Info

Title:
A just-in-time learning soft sensor modeling method based on the second-order similarity
作者:
祁成1 史旭东1 熊伟丽12
1. 江南大学 物联网工程学院,江苏 无锡 214122;
2. 江南大学 轻工过程先进控制教育部重点实验室,江苏 无锡 214122
Author(s):
QI Cheng1 SHI Xudong1 XIONG Weili12
1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2. Key Laboratory of Advanced Process Control for Light Industry Jiangnan University, Ministry of Education, Wuxi 214122, China
关键词:
即时学习更新频率二阶相似度相似度准则一阶相似度局部模型累计相似度因子相似度阈值
Keywords:
just-in-time learningupdate frequencysecond-order similaritysimilarity criterionfirst-order similaritylocal modelcumulative similarity factorsimilarity threshold
分类号:
TP273
DOI:
10.11992/tis.201809040
文献标志码:
A
摘要:
针对即时(惰性)学习模型频率降低间接导致的精度下降问题,提出一种二阶相似性的即时学习方法。该方法综合顾及到样本集的整体分布特性,在传统一阶相似度准则的基础上建立二阶相似度准则,采用与测试样本具有绝大部分相同近邻的二阶相似样本建立当前时刻的模型;同时将累计相似度因子用于建立局部模型时样本量的确定,并采用相似度阈值的方式判断此刻模型是否需要重新建立。该方法在青霉素发酵过程产物浓度的预测实验中得到了有效的验证。
Abstract:
Aiming at the indirect accuracy reduction caused by the frequency reduction of just-in-time (lazy) learning model, a second-order similarity just-in-time learning method is proposed. This method takes into account the overall distribution characteristics of the sample set, establishes a second-order similarity criterion based on the traditional first-order similarity criterion, and uses a second-order similarity sample with most of the same neighbors as the test sample to establish the model at the current time. At the same time, the cumulative similarity factor is used to determine the sample size when the local model is established, and the similarity threshold is used to determine whether the model needs to be rebuilt at this time. This method has been effectively validated in the prediction experiment of the product concentration in the fermentation process of penicillin.

参考文献/References:

[1] 汤健, 柴天佑, 刘卓, 等. 基于更新样本智能识别算法的自适应集成建模[J]. 自动化学报, 2016, 42(7): 1040-1052
TANG Jian, CHAI Tianyou, LIU Zhuo, et al. Adaptive ensemble modelling approach based on updating sample intelligent identification[J]. Acta automatica sinica, 2016, 42(7): 1040-1052
[2] ZHENG Junhua, SONG Zhihuan. Semisupervised learning for probabilistic partial least squares regression model and soft sensor application[J]. Journal of process control, 2018, 64: 123-131.
[3] LI Han, YOU Shijun, ZHANG Huan, et al. Analyzing the impact of heating emissions on air quality index based on principal component regression[J]. Journal of cleaner production, 2018, 171: 1577-1592.
[4] ZHENG Jianqiao, WANG Hongfang, ZHOU Hongpeng, et al. A using of just-in-time learning based data driven method in continuous stirred tank heater[C]//Proceedings of the 7th International Conference on Intelligent Control and Information Processing. Siem Reap, Cambodia, 2016: 98-104.
[5] PENG Xin, TANG Yang, HE Wangli, et al. A just-in-time learning based monitoring and classification method for hyper/hypocalcemia diagnosis[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2018, 15(3): 788-801.
[6] YIN Shen, GAO Huijun, QIU Jianbin, et al. Fault detection for nonlinear process with deterministic disturbances: a just-in-time learning based data driven method[J]. IEEE transactions on cybernetics, 2017, 47(11): 3649-3657.
[7] CHEN Kun, LIU Yi. Adaptive weighting just-in-time-learning quality prediction model for an industrial blast furnace[J]. ISIJ international, 2017, 57(1): 107-113.
[8] NIU Dapeng, GAO Huiyuan, LIU Yuanqing. Modeling of penicillin fermentation process based on FCM and improved Just-in-Time learning algorithm[C]//Proceedings of the 36th Chinese Control Conference. Dalian, China, 2017: 10328-10332.
[9] GE Zhiqiang, SONG Zhihuan. A comparative study of just-in-time-learning based methods for online soft sensor modeling[J]. Chemometrics and intelligent laboratory systems, 2010, 104(2): 306-317.
[10] 张宏伟, 李鹏飞, 景军锋, 等. 基于即时学习的软测量建模实时性改进[J]. 西安工程大学学报, 2014, 28(6): 750-754
ZHANG Hongwei, LI Pengfei, JING Junfeng, et al. A real-time performance improvement strategy of Just-In-Time-Learning based on soft sensor[J]. Journal of Xi’an Polytechnic University, 2014, 28(6): 750-754
[11] 牛大鹏, 刘元清. 基于改进即时学习算法的湿法冶金浸出过程建模[J]. 化工学报, 2017, 68(7): 2873-2879
NIU Dapeng, LIU Yuanqing. Modeling hydrometallurgical leaching process based on improved just-in-time learning algorithm[J]. CIESC journal, 2017, 68(7): 2873-2879
[12] CRIBBIN T. Discovering latent topical structure by second-order similarity analysis[J]. Journal of the American society for information science and technology, 2011, 62(6): 1188-1207.
[13] 刘毅, 金福江, 高增梁. 时变过程在线辨识的即时递推核学习方法研究[J]. 自动化学报, 2013, 39(5): 602-609
LIU Yi, JIN Fujiang, GAO Zengliang. Online identification of time-varying processes using just-in-time recursive kernel learning approach[J]. Acta automatica sinica, 2013, 39(5): 602-609
[14] LIU Yi, GAO Zengliang. Industrial melt index prediction with the ensemble anti-outlier just-in-time Gaussian process regression modeling method[J]. Journal of applied polymer science, 2015, 132(22): 41958.
[15] WANG Haijun, GAO Xinbo, ZHANG Kaibing, et al. Single image super-resolution using Gaussian process regression with dictionary-based sampling and student-t likelihood[J]. IEEE transactions on image processing, 2017, 26(7): 3556-3568.
[16] HAN Jianan, ZHANG Xiaoping, WANG Fang. Gaussian process regression stochastic volatility model for financial time series[J]. IEEE journal of selected topics in signal processing, 2016, 10(6): 1015-1028.
[17] XIONG Weili, SHI Xudong. Soft sensor modeling with a selective updating strategy for Gaussian process regression based on probabilistic principle component analysis[J]. Journal of the franklin institute, 2018, 355(12): 5336-5349.
[18] 何志昆, 刘光斌, 赵曦晶, 等. 高斯过程回归方法综述[J]. 控制与决策, 2013, 28(8): 1121-1129, 1137
HE Zhikun, LIU Guangbin, ZHAO Xijing, et al. Overview of Gaussian process regression[J]. Control and decision, 2013, 28(8): 1121-1129, 1137
[19] YU Jie. Multiway Gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes[J]. Industrial & engineering chemistry research, 2012, 51(40): 13227-13237.
[20] LIU Ziwei, GE Zhiqiang, CHEN Guangjie, et al. Adaptive soft sensors for quality prediction under the framework of Bayesian network[J]. Control engineering practice, 2018, 72: 19-28.

备注/Memo

备注/Memo:
收稿日期:2018-09-21。
基金项目:国家自然科学基金项目(61773182);江苏省自然科学基金项目(BK20170198)
作者简介:祁成,硕士研究生,主要研究方向为工业过程建模;史旭东,硕士研究生,主要研究方向为工业过程建模;熊伟丽,教授,博士。主要研究方向为复杂工业过程建模及优化、智能优化算法及应用。发表学术论文130余篇。
通讯作者:熊伟丽.E-mail:greenpre@163.com
更新日期/Last Update: 2021-01-15