Hesitant hierarchical T–S fuzzy system with fuzzily weighted recursive least square-Reference-Cited by-同舟云学术

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Hesitant hierarchical T–S fuzzy system with fuzzily weighted recursive least square

Published:2023-11 Issue: Volume:126 Page:106812
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Meng Lingyu,Xie Weixin,Li Liangqun,Li Yanshan,Liu Zongxiang

Funder

National Natural Science Foundation of China

Shenzhen Science and Technology Innovation Program

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference57 articles.

1. Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications, Vol. 2019;Andri,2019

2. An approach to online identification of Takagi–Sugeno fuzzy models;Angelov;IEEE Trans. Syst. Man Cybern. B,2004

3. Application of integrated factor evaluation–analytic hierarchy process–T-S fuzzy fault tree analysis in reliability allocation of industrial robot systems;Bai;Appl. Soft Comput.,2022

4. Online-review analysis based large-scale group decision-making for determining passenger demands and evaluating passenger satisfaction: Case study of high-speed rail system in China;Chen;Inf. Fusion,2021

5. K-means clustering for the aggregation of HFLTS possibility distributions: N-two-stage algorithmic paradigm;Chen;Knowl.-Based Syst.,2021

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Tracking Control Design for Takagi-Sugeno Fuzzy Systems Based on Membership Function-Dependent L∞ Performance;IEEE Access;2023

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