摘要
将粗糙集从原始数据中提取数据的能力和模糊神经网络的推理能力有效地集成起来。使用增量式规则提取算法(IREA)从原始数据中抽取构建模糊神经网络(FNN)所需的规则集。与传统的模糊神经网络相比较,使用IREA算法构建的FNN具有较短的规则长度和更少的规则条数。网络拥塞仿真试验验证了本文所述方法的优越性。
In this paper, rough set theorys ability of extracting crude domain knowledge in the form of rules from the data and FNNs ability of reasoning are combined to improve peoples ability of dealing with uncertainty, imprecision data. An incremental rule extraction algorithm (IREA) is utilized to construct IREA-based FNN. Compared with the classical FNN, IREAbased FNN has characteristics of fewer rules and shorter rule length. Network congestion prediction simulation demonstrates the superiority of the proposed IREA-based FNN over the classical FNN under the circumstance of no initial field knowledge.
出处
《系统仿真学报》
CAS
CSCD
2004年第5期1005-1008,共4页
Journal of System Simulation
基金
This work was supported by NNSFC (National Nature Science Foundation of China)
关键词
粗糙集
模糊神经网络
增量规则提取
网络拥塞
Rough Set
Fuzzy Neural Networks
Incremental Rule Extraction
Network Congestion
