Source:http://linkedlifedata.com/resource/pubmed/id/12918872
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
5
|
| pubmed:dateCreated |
2003-8-15
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| pubmed:abstractText |
This paper describes a method to combine near-infrared spectroscopy and a three layer back-propagation artificial neural network in order to identify official and unofficial rhubarbs. Thirty-three samples were taken as the training set, and 62 samples as the test set. The effects of input node number, learning rate and momentum on the final error and recognition accuracy for the training set, and on prediction accuracy for the test set were determined. A neural network with eight input nodes, a 0.5 learning rate, and a momentum of 0.3 can achieve a recognition accuracy of 100% for the training set and a prediction accuracy of 96.8% for the test set. The method described offers a quick and efficient means of identifying rhubarbs.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:status |
MEDLINE
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| pubmed:issn |
0958-0344
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:volume |
13
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
272-6
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| pubmed:dateRevised |
2003-11-14
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| pubmed:meshHeading | |
| pubmed:articleTitle |
The application of an artificial neural network in the identification of medicinal rhubarbs by near-infrared spectroscopy.
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| pubmed:affiliation |
School of Pharmaceutical Science, Peking University, Beijing, People's Republic of China.
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| pubmed:publicationType |
Journal Article
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