Abstract
An approach to identify data-driven interpretable and accurate fuzzy models is presented in this paper. Firstly, Gustafson-Kessel fuzzy clustering algorithm is used to identify initial fuzzy model, and cluster validity indices are adopted to determine the number of rules. Secondly, orthogonal least square method and similarity measure of fuzzy sets are utilized to reduce the initial fuzzy model and improve its interpretability. Thirdly, constraint Levenberg-Marquardt algorithm is used to optimize the reduced fuzzy model to improve its accuracy. The proposed approach is applied to PH neutralization process, and results show its validity. © Springer-Verlag Berlin Heidelberg 2005.
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CITATION STYLE
Xing, Z. Y., Zhang, Y., Jia, L. M., & Hu, W. L. (2006). Design of interpretable and accurate fuzzy models from data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3613 LNAI, pp. 69–78). Springer Verlag. https://doi.org/10.1007/11539506_9
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