INTELLIGENT PEARL DISEASE DIAGNOSIS BASED ON ROUGH SET - NEURAL NETWORK

被引：1
|
作者：
Xu, Longqin [1 ]
Liu, Shuangyin [1 ,2 ]
机构：
[1] Guangdong Ocean Univ, Coll Informat, Zhanjiang 524025, Guangdong, Peoples R China
[2] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China
来源：
INTELLIGENT AUTOMATION AND SOFT COMPUTING | 2012年 / 18卷 / 05期
关键词：
Intelligent Disease Diagnosis; rough set; neural network; reduction; pearl;
D O I：
10.1080/10798587.2012.10643257
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In view of large amount of monitoring data for Pearl disease, complexity of network structure of the traditional diagnostic neural network method, validity of disease data issues and slow training, this paper introduces the rough set theory to intelligent Pearl disease diagnosis. A method for disease diagnostics is proposed based on rough set - neural network. The rough set is used to remove the redundant attributes of decision table in order to reduce the number of input neurons and optimize neural network topology. The experimental simulation shows that the proposed algorithm can effectively improve the diagnostic rate and diagnostic accuracy. The proposed algorithm is a new way of methods for the diagnosis aquaculture technology
引用
收藏
页码：469 / 476
页数：8
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