Linked Life Data

16897522

Source:http://linkedlifedata.com/resource/pubmed/id/16897522

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1-3
pubmed:dateCreated
2006-11-2
pubmed:abstractText
Biodiversity studies in ecology often begin with the fitting and documentation of sampling data. This study is conducted to make function approximation on sampling data and to document the sampling information using artificial neural network algorithms, based on the invertebrate data sampled in the irrigated rice field. Three types of sampling data, i.e., the curve species richness vs. the sample size, the curve rarefaction, and the curve mean abundance of newly sampled species vs.the sample size, are fitted and documented using BP (Backpropagation) network and RBF (Radial Basis Function) network. As the comparisons, The Arrhenius model, and rarefaction model, and power function are tested for their ability to fit these data. The results show that the BP network and RBF network fit the data better than these models with smaller errors. BP network and RBF network can fit non-linear functions (sampling data) with specified accuracy and don't require mathematical assumptions. In addition to the interpolation, BP network is used to extrapolate the functions and the asymptote of the sampling data can be drawn. BP network cost a longer time to train the network and the results are always less stable compared to the RBF network. RBF network require more neurons to fit functions and generally it may not be used to extrapolate the functions. The mathematical function for sampling data can be exactly fitted using artificial neural network algorithms by adjusting the desired accuracy and maximum iterations. The total numbers of functional species of invertebrates in the tropical irrigated rice field are extrapolated as 140 to 149 using trained BP network, which are similar to the observed richness.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0167-6369
pubmed:author
pubmed:issnType
Print
pubmed:volume
122
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
185-201
pubmed:dateRevised
2009-5-11
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Function approximation and documentation of sampling data using artificial neural networks.
pubmed:affiliation
Research Institute of Entomology, School of Life Sciences, Zhongshan University, Guangzhou 510275, P.R. China. LS71@zsu.edu.cn
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't