.png){kind=icon}
返回
Evolutionary fuzzy modeling
.png){kind=icon}
PRE
AI
DOI：10.1109/TFUZZ.2003.817853.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This study is concerned with a general methodology of identification of fuzzy models. Unlike numeric models, fuzzy models operate at a level of information granules-fuzzy sets and this aspect brings up an important, design requirement of transparency of the model. We propose a three-phase development framework by distinguishing between structural and parametric optimization processes. The underlying topology of the model dwells on fuzzy neural networks-architectures governed by fuzzy logic and equipped with parametric flexibility. Two general optimization mechanisms are explored: the structural optimization is realized via Genetic Programming whereas for the ensuing detailed parametric optimization we proceed with gradient-based learning. The main advantages of this approach are discussed in detail. The study is illustrated with the aid of a numeric example that provides a detailed insight into the performance of the fuzzy models and quantifies crucial design issues.
Keyword:
evolutionary computing
fuzzy models
fuzzy neurons
genetic programming (GP)
learning logic architectures
rule-based computing
transparency
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:
11.9
论文数:
5.0K
被引数:
2.9W
机构
暂无机构信息
引用论文
The effects of poloxamer and sodium alginate mixture (Guardix-SG®) on range of motion after axillary lymph node dissection: A single-center, prospective, randomized, double-blind pilot study泊洛沙姆和海藻酸钠混合物的作用 (guardix-sg®) 关于腋窝淋巴结清扫术后的运动范围: 一项单中心，前瞻性，随机，双盲的先导研究.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏
查看更多内容