NDLI: Study on the Agricultural Expert System of Similarity Rough Set Optimizing Case Reasoning

Content Provider	IEEE Xplore Digital Library
Author	Longqin Xu Shuangyin Liu
Copyright Year	2009
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
Starting Page	454
Ending Page	458
File Size	429142
Page Count	5
File Format	PDF
ISBN	9780769538655
DOI	10.1109/ISCID.2009.121
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	the case feature weights Oceans container managed persistence Containers Educational institutions similarity rough set agricultural expert system Diagnostic expert systems attributes reduction Design optimization Diseases Insects case reasoning Set theory Expert systems Computational intelligence
Content Type	Text
Resource Type	Article

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