@inproceedings{de748865ae8c409b8619c92b8db66581,
title = "A rough T-S fuzzy model",
abstract = "A rough T-S fuzzy model that uses rough set to design the structure of T-S fuzzy model is proposed. Fuzzy c-means clustering is used to transform the continuous attributes to the discretized ones and partition the input space. Heuristic attribute reduction algorithm based on attribute significance deals with the discretized decision table to remove redundant condition attributes. Concise decision rules are extracted according to the threshold of degree of support, confidence and coverage. The rules of T-S fuzzy model are got according to the extracted decision rules. Antecedent parameters of T-S fuzzy model are determined according to fuzzy partition result, and consequent parameters are identified by least square method. Fuzzy rules of the proposed model have clear physical meaning and simplified structure. Moreover, a study algorithm is no longer needed to optimize the parameters of fuzzy model. Finally, the validity of the proposed model is verified by water treatment modeling experiment.",
keywords = "T-S fuzzy model, attributes reduction, fuzzy c-means clustering, rough sets, rules extraction",
author = "Li Wang and Zhou, {X. Z.} and Jie Shen",
year = "2012",
doi = "10.1109/WCICA.2012.6358399",
language = "英语",
isbn = "9781467313988",
series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",
pages = "3072--3076",
booktitle = "WCICA 2012 - Proceedings of the 10th World Congress on Intelligent Control and Automation",
note = "10th World Congress on Intelligent Control and Automation, WCICA 2012 ; Conference date: 06-07-2012 Through 08-07-2012",
}