.png){kind=icon}
返回
Fuzzy classification in dynamic environments
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-010-0657-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The persistence and evolution of systems essentially depend on their adaptivity to new situations. As an expression of intelligence, adaptivity is a distinguishing quality of any system that is able to learn and to adjust itself in a flexible manner to new environmental conditions and such ability ensures self-correction over time as new events happen, new input becomes available, or new operational conditions occur. This requires self-monitoring of the performance in an ever-changing environment. The relevance of adaptivity is established in numerous domains and by versatile real-world applications. The present paper presents an incremental fuzzy rule-based system for classification purposes. Relying on fuzzy min-max neural networks, the paper explains how fuzzy rules can be continuously online generated to meet the requirements of non-stationary dynamic environments, where data arrives over long periods of time. The approach proposed to deal with an ambient intelligence application. The simulation results show its effectiveness in dealing with dynamic situations and its performance when compared with existing approaches.
Keyword:
MECHANISMS
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
暂无机构信息
引用论文
分享收藏No Particle Mass Enhancement from Induced Atmospheric Ageing at a Rural Site in Northern Europe.png)
Atmosphere
OAAI
AtmosphereIF0
OAAI分享收藏
查看更多内容