Paper Info
Title
Study on the Agricultural Expert System of Similarity Rough Set Optimizing Case Reasoning
Abstract
In order to solve the problems caused by the shortage of subjectively preset case weight coefficient in the traditional case reasoning agricultural expert system and thus improve the accuracy and intellectualization of diagnosis and prevention of the system, an agricultural expert system of similarity-rough set optimizing case reasoning is constructed with the introduction of the attributes of similarity-rough set reducing case redundancy, this study focuses on the optimizing way of the case reasoning, the system structure design for data access based on Container-Managed Persistence mode, and the key technology of system functional mode and realization. Practices prove that this system can effectively resolve the problems of attribute redundancy in the plant diseases and insect pests case data, get rid of the noise inference, and simplify the case base, so as to improve the ability to diagnose and treat plant diseases and get easier access to maintenance and development
Year
DOI
Venue
2009
10.1109/ISCID.2009.121
ISCID (1)
Keywords
Field
DocType
agricultural expert system,insect pests case data,case base,system functional mode,plant disease,similarity rough set optimizing,case reasoning,subjectively preset case weight,optimizing case reasoning,case redundancy,traditional case reasoning,agriculture,information retrieval,set theory,rough set theory,expert system,redundancy,cognition,case based reasoning,data access,expert systems,accuracy,rough set
Data mining,Inference,Computer science,Expert system,Rough set,Redundancy (engineering),Artificial intelligence,Intellectualization,Case-based reasoning,Data access,Machine learning,Legal expert system
Conference
Citations 
PageRank 
References 
0
0.34
2
Authors
2
Name
Order
Citations
PageRank
Longqin Xu1344.64
Shuangyin Liu2305.89