A Novel Approach for Extraction of Fuzzy Rules Using the Neuro-fuzzy Network and Its Application in the Blending Process of Raw Slurry

Authors:
Rui Bai;Tianyou Chai;Enjie Ma
Affiliations:
Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang 110004,;Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang 110004, and Research Center of Automation, Northeastern University, Shenyang 1 ...;Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang 110004,
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

A novel approach is proposed to extract fuzzy rules from the inputoutput data using the neuro-fuzzy network combined the improved c-means clustering algorithm. Interpretability, which is one of the most important features of fuzzy system, is obtained using this approach. The fuzzy sets number of variables can also be determined appropriately using this approach. Finally, the proposed approach is applied to the blending process of raw slurry in the alumina sintering production process. The fuzzy system, which is used to determine the set values of the flow rate of materials, is extracted from the error of production index ---adjustment of the flow rate. Application results show that the fuzzy system not only improved the quality of raw slurry but also have good interpretability.