.png){kind=icon}
返回
A novel hybrid algorithm for function approximation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2006.09.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper introduces a novel hybrid algorithm for function approximation. The proposed algorithm consists of a hybrid approach to develop Takagi and Sugeno's fuzzy model for function approximation. In this paper, a coarse tuning based on Takagi and Sugeno's fuzzy model is applied to identify the fuzzy structure, and also a fuzzy cluster validity index is utilized to determine the optimal number of clusters. To obtain a more precision model, genetic algorithm (GA) and particle swarm optimization (PSO) are performed to conduct fine-tuning for the obtained parameter set of the premise parts and consequent parts in the aforementioned fuzzy model. The proposed algorithm is successfully applied to three tested examples. Compared with other existing approaches in the literature, the proposed algorithm is very useful for modeling function approximation. (c) 2006 Elsevier Ltd. All rights reserved.
Keyword:
fuzzy clustering
fuzzy model
hybrid algorithm
genetic algorithm
particle swarm optimization
function approximation
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:
7.5
论文数:
3.0W
被引数:
10.2W
机构
暂无机构信息
引用论文
.png){kind=icon}
Genetic algorithm based robust learning credit assignment cerebellar model articulation controller
PREAI
分享收藏分享收藏
查看更多内容