Function approximation and documentation of sampling data using artificial neural networks

Bibliographic Details
Main Authors: Zhang, Wenjun, Barrion, Albert
Format: Journal Article
Language:Inglés
Published: Springer 2006
Subjects:
Online Access:https://hdl.handle.net/10568/166576
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author Zhang, Wenjun
Barrion, Albert
author_browse Barrion, Albert
Zhang, Wenjun
author_facet Zhang, Wenjun
Barrion, Albert
author_sort Zhang, Wenjun
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publishDate 2006
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spelling CGSpace1665762024-12-19T14:12:50Z Function approximation and documentation of sampling data using artificial neural networks Zhang, Wenjun Barrion, Albert algorithms biodiversity documentation insect pests mathematical models neural networks sampling species richness statistical analysis 2006-11-01 2024-12-19T12:56:24Z 2024-12-19T12:56:24Z Journal Article https://hdl.handle.net/10568/166576 en Springer Zhang, Wenjun; Barrion, Albert. 2006. Function approximation and documentation of sampling data using artificial neural networks. Environ Monit Assess, Volume 122 no. 1-3 p. 185-201
spellingShingle algorithms
biodiversity
documentation
insect pests
mathematical models
neural networks
sampling
species richness
statistical analysis
Zhang, Wenjun
Barrion, Albert
Function approximation and documentation of sampling data using artificial neural networks
title Function approximation and documentation of sampling data using artificial neural networks
title_full Function approximation and documentation of sampling data using artificial neural networks
title_fullStr Function approximation and documentation of sampling data using artificial neural networks
title_full_unstemmed Function approximation and documentation of sampling data using artificial neural networks
title_short Function approximation and documentation of sampling data using artificial neural networks
title_sort function approximation and documentation of sampling data using artificial neural networks
topic algorithms
biodiversity
documentation
insect pests
mathematical models
neural networks
sampling
species richness
statistical analysis
url https://hdl.handle.net/10568/166576
work_keys_str_mv AT zhangwenjun functionapproximationanddocumentationofsamplingdatausingartificialneuralnetworks
AT barrionalbert functionapproximationanddocumentationofsamplingdatausingartificialneuralnetworks