NDLI: A New Approach for Fuzzy Fertilization Forecast Based on Support Vector Learning Mechanism

Content Provider	IEEE Xplore Digital Library
Author	Miao Li Dezhou Fang Jian Zhang Qiang Zhang
Copyright Year	2007
Description	Author affiliation: Chinese Acad. of Sci., Hefei (Miao Li; Dezhou Fang; Jian Zhang; Qiang Zhang)
Abstract	This paper presents a support vector machine (SVM) algorithm to generate fertilization fuzzy rules so as to realize the prediction method. That SVM is equivalent to the fuzzy rule-based modeling (FRM) is important to many practical and complicated situations where one unable to determine the number of rules in advance, such as knowledge acquisition form samples. Agricultural fertilizer commonly used orthogonal experiment whose balanced scattered data makes the regression curve fitting method ineffective. This paper presents training data set for SVM learning, and takes advantage of membership to extract fuzzy IF-THEN rules. Rule activated by the threshold value and credibility controls the prediction process. The approach not only avoids the error caused by regression method, and the fuzzy rules also increase the linguistic interpretability of the generated rules, and improve the capability of knowledge acquisition greatly. The performance of the proposed approach is compared to the linear regression method by orthogonal experiment.
Starting Page	321
Ending Page	325
File Size	349441
Page Count	5
File Format	PDF
ISBN	9780769528748
DOI	10.1109/FSKD.2007.68
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Support vector machines Fuzzy sets Knowledge acquisition Scattering Training data Prediction methods Data mining Curve fitting Fertilizers
Content Type	Text
Resource Type	Article

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