Design and application of Nagar-Bardini structure-based interval type-2 fuzzy logic systems optimized with the combination of backpropagation algorithms and recursive least square algorithms

被引：13

作者：

Chen, Yang ^{[1
]}

Li, Chenxi ^{[1
]}

Yang, Jiaxiu ^{[1
]}

机构：

[1] Liaoning Univ Technol, Jinzhou, Peoples R China

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2023年 / 211卷

基金：

中国国家自然科学基金;

关键词：

Interval type-2 fuzzy logic systems (IT2 FLSs); backpropagation (BP) algorithms; Convergence; Recursive least square (RLS) algorithms; Simulation; CENTROID TYPE-REDUCTION; CONTROLLER; SPEED; ROBOT;

D O I：

10.1016/j.eswa.2022.118596

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper designs a kind of Nagar-Bardini structure based on interval type-2 (IT2) fuzzy logic systems for permanent magnetic drive uncertain parameter forecasting. For each fuzzy rule, the consequent, antecedent and input measurement of IT2 membership functions (MFs) are selected as the Gaussian type-2 MFs with uncertain standard deviations. Backpropagation algorithms and recursive least square algorithms are used to optimize the antecedent, input measurement and consequent parameters of fuzzy logic system (FLS) forecasters, respectively. Compared with the corresponding singleton and non-singleton type-1 (T1) FLSs, two Monte Carlo simulation instances on the basis of data of PMD process show the effectiveness and superiority of non-singleton IT2 FLSs according to the convergence analysis.

引用

页数：10

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