@inproceedings{d64c53cc91be470198ee6674faedf750,
title = "Using Parallel Algorithm to Speedup the Rules Learning Process of a Type-2 Fuzzy Logic System",
abstract = "Since a type-2 fuzzy logic system (T2FLS) needs to perform type-reduction calculation, and has more parameters compare to a type-1 fuzzy logic system, the rules learning process of a T2FLS using a serial algorithm is relatively time-consuming. In this paper, a data parallel method is designed to construct a parallel algorithm for the rules learning of a T2FLS. Numerical experiments show that the proposed parallel algorithm is faster than a serial one, and also gets better performance. Furthermore, to simplify the process of deriving the formula of parameter-updating for a FLS by using error back-propagation (BP), a program package for rules learning of a FLS based on Tensorflow{\textquoteright}s automatic differentiation function in Python environment is provided. One can obtain it for free from the github address: https://github.com/wangjiaw123/wjwRepository\_test.",
keywords = "Parallel algorithms, Program package for fuzzy logic system, Rule learning, Tensorflow, Type-2 fuzzy logic system",
author = "Wang, \{Jia Wen\} and Zhang, \{Qi Ye\}",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 17th Chinese Intelligent Systems Conference, CISC 2021 ; Conference date: 16-10-2021 Through 17-10-2021",
year = "2022",
doi = "10.1007/978-981-16-6320-8\_15",
language = "英语",
isbn = "9789811663192",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "141--151",
editor = "Yingmin Jia and Weicun Zhang and Yongling Fu and Zhiyuan Yu and Song Zheng",
booktitle = "Proceedings of 2021 Chinese Intelligent Systems Conference",
address = "德国",
}