Research on soft-sensor based on support vector regression for particle size of grinding and classification process

Zhang, Yong; Liang, Yunqi

doi:10.1109/ccdc.2016.7532204

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…erefore, more experts and scholars are willing to study neural network models to set up soft-sensing models for grinding and classifying process. Common neural network soft-sensing models include BP neural network [15,24,25], RBF neural network [26][27][28][29][30], support vector machine (SVM) [31,32], and extreme learning machine (ELM) [33]. e traditional training methods of these neural networks often cannot find suitable model parameters, which makes the prediction accuracy of the model unable to be further improved.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm

et al. 2020

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Soft-sensor technology plays a vital role in tracking and monitoring the key production indicators of the grinding and classifying process. Least squares support vector machine (LSSVM), as a soft-sensor model with strong generalization ability, can be used to predict key production indicators in complex grinding processes. The traditional crossvalidation method cannot obtain the ideal structure parameters of LSSVM. In order to improve the prediction accuracy of LSSVM, a golden sine Harris Hawk optimization (GSHHO) algorithm was proposed to optimize the structure parameters of LSSVM models with linear kernel, sigmoid kernel, polynomial kernel, and radial basis kernel, and the influences of GSHHO algorithm on the prediction accuracy under these LSSVM models were studied. In order to deal with the problem that the prediction accuracy of the model decreases due to changes of industrial status, this paper adopts moving window (MW) strategy to adaptively revise the LSSVM (MW-LSSVM), which greatly improves the prediction accuracy of the LSSVM. The prediction accuracy of the regularized extreme learning machine with MW strategy (MW-RELM) is higher than that of MW-LSSVM at some moments. Based on the training errors of LSSVM and RELM within the window, this paper proposes an adaptive hybrid soft-sensing model that switches between LSSVM and RELM. Compared with the previous MW-LSSVM, MW-neural network trained with extended Kalman filter(MW-KNN), and MW-RELM, the prediction accuracy of the hybrid model is further improved. Simulation results show that the proposed hybrid adaptive soft-sensor model has good generalization ability and prediction accuracy.

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Section: Introductionmentioning

confidence: 99%

Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm

et al. 2020

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A Soft Sensor Model for Cement Specific Surface Area Based on TCN-ASRU Neural Network

Sun¹,

Zhang²,

Zhao³

et al. 2023

IEEE Trans. Instrum. Meas.

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State of the Art Review on Process, System, and Operations Control in Modern Manufacturing

Djurdjanović

Mears

Niaki

et al. 2018

Journal of Manufacturing Science and Engineering

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Dramatic advancements and adoption of computing capabilities, communication technologies, and advanced, pervasive sensing have impacted every aspect of modern manufacturing. Furthermore, as society explores the Fourth Industrial Revolution characterized by access to and leveraging of knowledge in the manufacturing enterprise, the very character of manufacturing is rapidly evolving, with new, more complex processes, and radically, new products appearing in both the industries and academe. As for traditional manufacturing processes, they are also undergoing transformations in the sense that they face ever-increasing requirements in terms of quality, reliability, and productivity, needs that are being addressed in the knowledge domain. Finally, across all manufacturing we see the need to understand and control interactions between various stages of any given process, as well as interactions between multiple products produced in a manufacturing system. All these factors have motivated tremendous advancements in methodologies and applications of control theory in all aspects of manufacturing: at process and equipment level, manufacturing systems level, and operations level. Motivated by these factors, the purpose of this paper is to give a high-level overview of latest progress in process and operations control in modern manufacturing. Such a review of relevant work at various scales of manufacturing is aimed not only to offer interested readers information about state-of-the art in control methods and applications in manufacturing, but also to give researchers and practitioners a vision about where the direction of future research may be, especially in light of opportunities that lay as one concurrently looks at the process, system and operation levels of manufacturing.

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Research on soft-sensor based on support vector regression for particle size of grinding and classification process

Cited by 3 publications

References 2 publications

Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm

Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm

A Soft Sensor Model for Cement Specific Surface Area Based on TCN-ASRU Neural Network

State of the Art Review on Process, System, and Operations Control in Modern Manufacturing

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