<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper proposes a knowledge-based model applied to an experimental scale evaporative cane sugar crystallization process, which combines the methods of offline and online knowledge acquisition. Firstly, a data mining method based on rough set theory is utilized to extract information from the large quantity of relevant data obtained in experiment. This method products an offline predictive knowledge. Thereafter, a method for online knowledge learning and self-improvement is put forward, based on support vector machine with particle swarm optimization, to improve the predictive accuracy and generalization capacity. Furthermore, the intelligent system is tested using a selfregulating intelligent comprehensive monitoring and controlling platform that represents the cane sugar process. Results demonstrate the feasibility of the system for predicting the crystallization state in a real cane sugar process.</p></abstract>

This paper proposes a knowledge-based model applied to an experimental scale evaporative cane sugar crystallization process, which combines the methods of offline and online knowledge acquisition. Firstly, a data mining method based on rough set theory is utilized to extract information from the large quantity of relevant data obtained in experiment. This method products an offline predictive knowledge. Thereafter, a method for online knowledge learning and self-improvement is put forward, based on support vector machine with particle swarm optimization, to improve the predictive accuracy and generalization capacity. Furthermore, the intelligent system is tested using a selfregulating intelligent comprehensive monitoring and controlling platform that represents the cane sugar process. Results demonstrate the feasibility of the system for predicting the crystallization state in a real cane sugar process.

Knowledge-Based Modeling For Predicting Cane Sugar Crystallization State

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Knowledge-Based Modeling For Predicting Cane Sugar Crystallization State"}

This paper proposes a knowledge-based model applied to an experimental scale evaporative cane sugar crystallization process, which combines the methods of...

Knowledge-Based Modeling For Predicting Cane Sugar Crystallization State

Published Online: Sep 01, 2014

Page range: 942 - 965

Received: Mar 10, 2014

Accepted: Sep 01, 2014

DOI: https://doi.org/10.21307/ijssis-2017-689

Keywords
Cane sugar crystallization state, intelligent system, knowledge acquisition, rough set, support vector machine

© 2014 Yanmei Meng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Knowledge-Based Modeling For Predicting Cane Sugar Crystallization State

Published Online: Sep 01, 2014

Page range: 942 - 965

Received: Mar 10, 2014

Accepted: Sep 01, 2014

DOI: https://doi.org/10.21307/ijssis-2017-689

KeywordsCane sugar crystallization state, intelligent system, knowledge acquisition, rough set, support vector machine

© 2014 Yanmei Meng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Cane sugar crystallization state, intelligent system, knowledge acquisition, rough set, support vector machine