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.
This project is supported by the National Foundamental Research Program of China(Grant No. 2002CB312201), and the State Key Program of National Natural Science of China(Grant No.60534010), and the Funds for Creative Research Groups of China (Grant No. 60521003), and the Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT0421).
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Bai, R., Chai, T., Ma, E. (2007). A Novel Approach for Extraction of Fuzzy Rules Using the Neuro-fuzzy Network and Its Application in the Blending Process of Raw Slurry. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_44
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DOI: https://doi.org/10.1007/978-3-540-72393-6_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
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