.png){kind=icon}
返回
A fuzzy C-regression model algorithm using a new PSO algorithm
.png){kind=icon}
PRE
AI
DOI：10.1002/acs.2829.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a new methodology is introduced for the identification of the parameters of the multiple-input-multiple-output local linear Takagi-Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi-Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature.
Keyword:
chaos adaptive particle swarm optimization
fuzzy C-regression model clustering algorithm
identification
multiple-input multiple-output
Takagi-Sugeno fuzzy models
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.8
论文数:
2.6K
被引数:
3.6K
机构
引用论文
.png){kind=icon}
Conditional Survival of Patients With the Four Major Histologic Subgroups of Lung Cancer in Denmark
PREAI
分享收藏
查看更多内容