A novel fuzzy logic system with consequents as fuzzy weighted averages of antecedents

Qiye Zhang; Yuqing Liu; Xiao Tian

doi:10.1007/978-981-13-2291-4_56

A novel fuzzy logic system with consequents as fuzzy weighted averages of antecedents

Qiye Zhang
, Yuqing Liu^*
, Xiao Tian

^*Corresponding author for this work

School of Mathematical Sciences

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

2 Scopus citations

Abstract

Fuzzy logic system is an intelligent system based on IF-THEN rules, which can handle uncertainties effectively, and has been applied to various fields. The design of rules is a key step when a fuzzy logic system is modelled in a practical situation. In this paper, a novel fuzzy logic system named FWA with novel rules is proposed, in which the consequents are fuzzy weighted averages of antecedents. The proposed rules establish some relationship between consequents and antecedents in advance, so that the proposed FWA fuzzy logic system will reduce training time, improve training efficiency, and optimize parameters faster.

Original language	English
Title of host publication	Lecture Notes in Electrical Engineering
Publisher	Springer Verlag
Pages	571-582
Number of pages	12
DOIs	https://doi.org/10.1007/978-981-13-2291-4_56
State	Published - 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	529
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Keywords

Error back-propagation
Fuzzy logic system
Fuzzy weighted average
Steepest descent algorithm
Trapezoidal fuzzy number

Access to Document

10.1007/978-981-13-2291-4_56

Cite this

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abstract = "Fuzzy logic system is an intelligent system based on IF-THEN rules, which can handle uncertainties effectively, and has been applied to various fields. The design of rules is a key step when a fuzzy logic system is modelled in a practical situation. In this paper, a novel fuzzy logic system named FWA with novel rules is proposed, in which the consequents are fuzzy weighted averages of antecedents. The proposed rules establish some relationship between consequents and antecedents in advance, so that the proposed FWA fuzzy logic system will reduce training time, improve training efficiency, and optimize parameters faster.",

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