Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10165866
Weitai Gong, Jianxiang Zhang, X. Dai, Bo Tian
The iterative learning control problem of moving boundary distributed parameter systems with control delay under sensor/actuator networks is studied. A P-type iterative learning algorithm with known delays is proposed. The convergence of linear systems with sensor/actuator networks is proved by using compression mapping principle. In order to further verify the feasibility of the algorithm the nonlinear system with control delay is also considered, and its convergence is proved by strict mathematical analysis. Through strict mathematical analysis, the condition of convergence of output error is obtained. Numerical results show the effectiveness of the proposed method.
{"title":"Iterative learning control for moving boundary distributed parameter systems with control delays under sensor/actuator networks","authors":"Weitai Gong, Jianxiang Zhang, X. Dai, Bo Tian","doi":"10.1109/DDCLS58216.2023.10165866","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10165866","url":null,"abstract":"The iterative learning control problem of moving boundary distributed parameter systems with control delay under sensor/actuator networks is studied. A P-type iterative learning algorithm with known delays is proposed. The convergence of linear systems with sensor/actuator networks is proved by using compression mapping principle. In order to further verify the feasibility of the algorithm the nonlinear system with control delay is also considered, and its convergence is proved by strict mathematical analysis. Through strict mathematical analysis, the condition of convergence of output error is obtained. Numerical results show the effectiveness of the proposed method.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127201737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166105
Yulin Yang, Shan Liu
Aiming at the low robustness of image feature extractor in Image-Based Visual Servo (IBVS), a robot visual servo method based on object detection neural network YOLOv3 is proposed. By improving the output layer of YOLOv3 and adding attitude angle of camera, the pixel coordinate and depth information of feature points, the robustness of the IBVS system based on point features is improved while it can cope with multi-type and multi-instance objects, and the problem of the image Jacoby matrix falling into singularity caused by excessive rotation angle error of the optical axis is avoided. The visual servo convergence is accelerated. Meanwhile, the network training data generation algorithm of the desired image is used to replace the traditional manual data annotation, which reduces the cost of data acquisition, and the data enhancement method ensures the generalization performance of the training model.
{"title":"Multi-Object Robot Visual Servo Based on YOLOv3","authors":"Yulin Yang, Shan Liu","doi":"10.1109/DDCLS58216.2023.10166105","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166105","url":null,"abstract":"Aiming at the low robustness of image feature extractor in Image-Based Visual Servo (IBVS), a robot visual servo method based on object detection neural network YOLOv3 is proposed. By improving the output layer of YOLOv3 and adding attitude angle of camera, the pixel coordinate and depth information of feature points, the robustness of the IBVS system based on point features is improved while it can cope with multi-type and multi-instance objects, and the problem of the image Jacoby matrix falling into singularity caused by excessive rotation angle error of the optical axis is avoided. The visual servo convergence is accelerated. Meanwhile, the network training data generation algorithm of the desired image is used to replace the traditional manual data annotation, which reduces the cost of data acquisition, and the data enhancement method ensures the generalization performance of the training model.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130054596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10167265
Yajuan Yao, S. Jin, Qian Wang
An improved long short-term memory (LSTM) model based on ensemble empirical mode decomposition (EEMD) is designed for short-term passenger flow prediction in view of the complex dynamics, uncertainty and prediction difficulty of subway inbound passenger flow. First, the raw data is decomposed into several stationary components and a residue by EEMD method. Then, a combination of high-correlation components and a combination of low-correlation components obtained by calculating Pearson Correlation Coefficient between each component and the raw data are combined with date feature to form the input set of LSTM neural network. And the predicted passenger flow data is the output set. Finally, compared with the single LSTM model, the trained EEMD-LSTM model is better according to the metrics, and the absolute error of the EEMD-LSTM model is significantly lower during the peak passenger flows. The experimental results of Tiantongyuan Station of Beijing Metro Line 5 show that the improved model can effectively improve the prediction accuracy, which is conducive to the dynamic adjustment of station management plan.
{"title":"Subway Short-term Passenger Flow Prediction Based on Improved LSTM","authors":"Yajuan Yao, S. Jin, Qian Wang","doi":"10.1109/DDCLS58216.2023.10167265","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10167265","url":null,"abstract":"An improved long short-term memory (LSTM) model based on ensemble empirical mode decomposition (EEMD) is designed for short-term passenger flow prediction in view of the complex dynamics, uncertainty and prediction difficulty of subway inbound passenger flow. First, the raw data is decomposed into several stationary components and a residue by EEMD method. Then, a combination of high-correlation components and a combination of low-correlation components obtained by calculating Pearson Correlation Coefficient between each component and the raw data are combined with date feature to form the input set of LSTM neural network. And the predicted passenger flow data is the output set. Finally, compared with the single LSTM model, the trained EEMD-LSTM model is better according to the metrics, and the absolute error of the EEMD-LSTM model is significantly lower during the peak passenger flows. The experimental results of Tiantongyuan Station of Beijing Metro Line 5 show that the improved model can effectively improve the prediction accuracy, which is conducive to the dynamic adjustment of station management plan.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130135230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166693
Xiaoyu Jiang, Le Yao, Zeyu Yang, Zhihuan Song, Bingbing Shen
In practice, data-driven soft sensors often face data shortages in modeling. Data augmentation technology has offered a feasible solution for this problem in recent years. However, how to better use virtual data for data augmentation is still an open topic. In this paper, a novel data augmentation soft sensing method is proposed. It uses Gaussian mixture models (GMM) to generate virtual data for the training dataset, and developed a double-weighted neural network (dwDNN) for weighted regression modeling. On top of that, the Bayesian optimization algorithm is applied to the weight selection of dwDNN to further enhance the efficiency and effectiveness of GMM -dwDNN on virtual data. In the end, a real industrial case is used to illustrate the superiority of the proposed approach in soft sensing.
{"title":"Gaussian Mixture Model and Double-Weighted Deep Neural Networks for Data Augmentation Soft Sensing","authors":"Xiaoyu Jiang, Le Yao, Zeyu Yang, Zhihuan Song, Bingbing Shen","doi":"10.1109/DDCLS58216.2023.10166693","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166693","url":null,"abstract":"In practice, data-driven soft sensors often face data shortages in modeling. Data augmentation technology has offered a feasible solution for this problem in recent years. However, how to better use virtual data for data augmentation is still an open topic. In this paper, a novel data augmentation soft sensing method is proposed. It uses Gaussian mixture models (GMM) to generate virtual data for the training dataset, and developed a double-weighted neural network (dwDNN) for weighted regression modeling. On top of that, the Bayesian optimization algorithm is applied to the weight selection of dwDNN to further enhance the efficiency and effectiveness of GMM -dwDNN on virtual data. In the end, a real industrial case is used to illustrate the superiority of the proposed approach in soft sensing.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131028787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10165995
Yanlin He, Shuaifeng Guo, Yuan Xu, Qun Zhu
In today's industrial processes, data-driven soft sensors are a frequently used tool for predicting quality variables. Autoencoder (AE) is an unsupervised algorithm which can extract latent features from initial data. However, during the feature extraction process, the traditional autoencoder does not consider the correlation between modeling input variables and quality variables to be predicted. To solve this issue, a novel autoencoder based on variable correlation analysis (VCA-AE) is proposed. In VCA-AE, the correlation of modeling input variables and quality variables to be predicted is performed by correlation analysis, and input variables are divided into two parts, which are input to the sub-autoencoder to extract latent features, respectively. In each sub-autoencoder, input variables and quality variables have the same correlation. Next, a feedforward neural network Extreme Learning Machine (ELM) is used to develop soft sensor model based on the extracted latent feature variables and quality variables. Finally, the effectiveness of the proposed soft sensor model combining VCA-AE and ELM is illustrated by an experiment of the industrial PTA process.
{"title":"Novel Autoencoder Based on Variable Correlation Analysis for Industrial Soft Sensing","authors":"Yanlin He, Shuaifeng Guo, Yuan Xu, Qun Zhu","doi":"10.1109/DDCLS58216.2023.10165995","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10165995","url":null,"abstract":"In today's industrial processes, data-driven soft sensors are a frequently used tool for predicting quality variables. Autoencoder (AE) is an unsupervised algorithm which can extract latent features from initial data. However, during the feature extraction process, the traditional autoencoder does not consider the correlation between modeling input variables and quality variables to be predicted. To solve this issue, a novel autoencoder based on variable correlation analysis (VCA-AE) is proposed. In VCA-AE, the correlation of modeling input variables and quality variables to be predicted is performed by correlation analysis, and input variables are divided into two parts, which are input to the sub-autoencoder to extract latent features, respectively. In each sub-autoencoder, input variables and quality variables have the same correlation. Next, a feedforward neural network Extreme Learning Machine (ELM) is used to develop soft sensor model based on the extracted latent feature variables and quality variables. Finally, the effectiveness of the proposed soft sensor model combining VCA-AE and ELM is illustrated by an experiment of the industrial PTA process.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"80 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130648547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166043
Zhiyong Zhan, Yang Zhou, Li Jia, Yilin Zhao
This paper proposes a new method of monotonicity, which is used to solve the overfitting problem of the Long-Short-Term Memory (LSTM) model. The main contribution of this paper is applying the monotonicity as priori knowledge to the modeling process. This study uses scatter plots to describe bivariate variables and the Spearman coefficient to extract the monotonicity of data. To exclude most noise point, the scatter diagram is filtered by a binary 0–1 liner program. Base on the monotonicity of data have known, an optimization problem with constraint is proposed to obtain the LSTM neural network model. An experiment of ethylene cracking show that the proposed method can achieve a good predicting performance and less overfitting effects.
{"title":"Long Short-term Memory modeling method with monotonicity analysis as constraints base on Spearman coefficient","authors":"Zhiyong Zhan, Yang Zhou, Li Jia, Yilin Zhao","doi":"10.1109/DDCLS58216.2023.10166043","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166043","url":null,"abstract":"This paper proposes a new method of monotonicity, which is used to solve the overfitting problem of the Long-Short-Term Memory (LSTM) model. The main contribution of this paper is applying the monotonicity as priori knowledge to the modeling process. This study uses scatter plots to describe bivariate variables and the Spearman coefficient to extract the monotonicity of data. To exclude most noise point, the scatter diagram is filtered by a binary 0–1 liner program. Base on the monotonicity of data have known, an optimization problem with constraint is proposed to obtain the LSTM neural network model. An experiment of ethylene cracking show that the proposed method can achieve a good predicting performance and less overfitting effects.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"355 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132195735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166738
Zunguan Fan, Kang Wang, X. Li
The quality of flotation conditions directly affects the flotation efficiency. Aiming at the problems of difficult online detection, strong subjective arbitrariness, and low recognition efficiency of various flotation conditions in actual flotation work, a flotation condition recognition method based on hypergraph neural network (HGNN) and dynamic feature of forth images is proposed in this paper. Firstly, an improved local binary mode (LBP-TOP) algorithm is introduced to extract the dynamic features of forth sequence containing time information, and then features such as kurtosis and skewness are extracted as supplements to integrate the dynamic features of forth with the supplementary features. By utilizing the aforementioned characteristics and constructing a hypergraph, we have developed an HGNN model that facilitates high-order complex data correlation encoding, thus accomplishing accurate identification of flotation conditions. Finally, simulation shows the effectiveness of the proposed method.
{"title":"Flotation Condition Recognition Based on HGNN and Forth Image Dynamic Feature","authors":"Zunguan Fan, Kang Wang, X. Li","doi":"10.1109/DDCLS58216.2023.10166738","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166738","url":null,"abstract":"The quality of flotation conditions directly affects the flotation efficiency. Aiming at the problems of difficult online detection, strong subjective arbitrariness, and low recognition efficiency of various flotation conditions in actual flotation work, a flotation condition recognition method based on hypergraph neural network (HGNN) and dynamic feature of forth images is proposed in this paper. Firstly, an improved local binary mode (LBP-TOP) algorithm is introduced to extract the dynamic features of forth sequence containing time information, and then features such as kurtosis and skewness are extracted as supplements to integrate the dynamic features of forth with the supplementary features. By utilizing the aforementioned characteristics and constructing a hypergraph, we have developed an HGNN model that facilitates high-order complex data correlation encoding, thus accomplishing accurate identification of flotation conditions. Finally, simulation shows the effectiveness of the proposed method.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130889497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Session-based recommendation is nowadays increasingly popular in e-commerce, aiming at predicting the next action of a user under anonymous sessions. Previous research methods on session recommendation model the temporal information inherent in a session as a sequence or graph, however, they disregard the session's graph context information, as well as the relationship between the user and the target object, which affects the accuracy of the recommendation. To obtain the rich graph context information in session recommendation and the intrinsic connection between target items and users, we propose a graph context target attention graph neural network for session-based recommendation, which uses a self-attentive network and graph neural network to extract the item embedding of graph context information; the target attention then adaptively stimulates various user interests. Experimental results on two real-world datasets demonstrate that our proposed model outperforms other comparison algorithms on the evaluation metrics of Recall@20 and MRR@20 in session-based recommendation.
{"title":"Graph Context Target Attention Graph Neural Network for Session-based Recommendation","authors":"Jiale Chen, Xing Xing, Yongjie Niu, Xuanming Zhang, Zhichun Jia","doi":"10.1109/DDCLS58216.2023.10166209","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166209","url":null,"abstract":"Session-based recommendation is nowadays increasingly popular in e-commerce, aiming at predicting the next action of a user under anonymous sessions. Previous research methods on session recommendation model the temporal information inherent in a session as a sequence or graph, however, they disregard the session's graph context information, as well as the relationship between the user and the target object, which affects the accuracy of the recommendation. To obtain the rich graph context information in session recommendation and the intrinsic connection between target items and users, we propose a graph context target attention graph neural network for session-based recommendation, which uses a self-attentive network and graph neural network to extract the item embedding of graph context information; the target attention then adaptively stimulates various user interests. Experimental results on two real-world datasets demonstrate that our proposed model outperforms other comparison algorithms on the evaluation metrics of Recall@20 and MRR@20 in session-based recommendation.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130450678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166016
Yanli Liu, Yihua Sun, Liying Hao
A command filter based finite-time heading control scheme of intelligent ship with asymmetric output constraints is developed. Firstly, asymmetric output constraints are handled via the nonlinear state-dependent function. Then, the finite-time command filters are utilized to filter the immediate control function. This method can solve the issue of calculating burden with good effect. Subsequently, the finite-time error compensate signals are established to make up for the filtering error. Under the constructed tactic, system output does not violate the constraint conditions. Additionally, by analysis of the Lyapunov function and immediate control function, all closed-loop signals are bounded, the heading tracking error can converge to zero in finite time. And the validity of the tactic is confirmed on the simulations in the end.
{"title":"Adaptive Finite-Time Heading Control of Intelligent Ship with Asymmetric Output Constraints","authors":"Yanli Liu, Yihua Sun, Liying Hao","doi":"10.1109/DDCLS58216.2023.10166016","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166016","url":null,"abstract":"A command filter based finite-time heading control scheme of intelligent ship with asymmetric output constraints is developed. Firstly, asymmetric output constraints are handled via the nonlinear state-dependent function. Then, the finite-time command filters are utilized to filter the immediate control function. This method can solve the issue of calculating burden with good effect. Subsequently, the finite-time error compensate signals are established to make up for the filtering error. Under the constructed tactic, system output does not violate the constraint conditions. Additionally, by analysis of the Lyapunov function and immediate control function, all closed-loop signals are bounded, the heading tracking error can converge to zero in finite time. And the validity of the tactic is confirmed on the simulations in the end.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126459391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166881
Xiaowen Zhang, Kai Zhang, Kai-xiang Peng
This paper reviews the prediction methods of multiple linear regression models least squares (LS), Partial least squares (PLS), and higher order partial least squares (HOPLS) and compares the characteristics of these three methods. The methods are applied to the hot rolling mill process. Three kinds of methods are used to predict the exit thickness of finishing rolling steel plates with different thickness specifications. The mean absolute error (MAE), root mean square error (RMSE), and the percentage of the number of samples whose prediction error is within ±3% of the measured value in the total number of predicted samples are used as indices of performance to compare the thickness predicted performance. The experimental results show that HOPLS has better prediction accuracy and generalization performance compared with the other considered methods.
{"title":"A Comparison of LS-based Steel Thickness Prediction Methods for a Hot Rolling Mill Process","authors":"Xiaowen Zhang, Kai Zhang, Kai-xiang Peng","doi":"10.1109/DDCLS58216.2023.10166881","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166881","url":null,"abstract":"This paper reviews the prediction methods of multiple linear regression models least squares (LS), Partial least squares (PLS), and higher order partial least squares (HOPLS) and compares the characteristics of these three methods. The methods are applied to the hot rolling mill process. Three kinds of methods are used to predict the exit thickness of finishing rolling steel plates with different thickness specifications. The mean absolute error (MAE), root mean square error (RMSE), and the percentage of the number of samples whose prediction error is within ±3% of the measured value in the total number of predicted samples are used as indices of performance to compare the thickness predicted performance. The experimental results show that HOPLS has better prediction accuracy and generalization performance compared with the other considered methods.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121397290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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