A fuzzy C-regression model algorithm using a new PSO algorithm-Reference-Cited by-同舟云学术

A fuzzy C-regression model algorithm using a new PSO algorithm

Published:2017-10-10 Issue:1 Volume:32 Page:115-133
ISSN:0890-6327
Container-title:International Journal of Adaptive Control and Signal Processing
language:en
Short-container-title:Int J Adapt Control Signal Process

Author:

Taieb Adel¹^ORCID,Soltani Moez¹,Chaari Abdelkader¹

Affiliation:

1. Laboratory of Engineering of Industrial Systems and Renewable Energy; National Higher Engineering School of Tunis (ENSIT); 5, av. Taha Hussein, Tunis, BP 56-1008 Tunis Tunisia

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Signal Processing,Control and Systems Engineering

Reference33 articles.

1. Fuzzy Modeling for Control

2. Fuzzy sliding mode PI controller for nonlinear systems;Lagrat;WSEAS Trans Signal Process,2006

3. Identification of nonlinear multivariable systems by adaptive fuzzy Takagi-Sugeno model;Trabelsi;International Journal of Computational Cognition,2004

4. A hybrid clustering and gradient descent approach for fuzzy modeling;Wong;IEEE Trans Syst Man Cybern,1999

5. Fuzzy Control

Cited by 7 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. Optimized parametrization of adaptive controllers for enhanced current regulation in grid‐tied converters;International Journal of Adaptive Control and Signal Processing;2023-10-10

2. Wind Speed Prediction Using a Robust Possibilistic C‐Regression Model Method: A Case Study of Tunisia;Cyberphysical Smart Cities Infrastructures;2021-12-17

3. Approximation of two-variable functions using high-order Takagi–Sugeno fuzzy systems, sparse regressions, and metaheuristic optimization;Soft Computing;2020-09-05

4. Sparse regressions and particle swarm optimization in training high-order Takagi–Sugeno fuzzy systems;Neural Computing and Applications;2020-07-08

5. Training High-Order Takagi-Sugeno Fuzzy Systems Using Batch Least Squares and Particle Swarm Optimization;International Journal of Fuzzy Systems;2019-11-14