Determining temporal pattern of community dynamics by using unsupervised learning algorithms

Determining temporal pattern of community dynamics by using unsupervised learning algorithms Analysis of patterns of temporal variation in community dynamics was conducted by combining two unsupervised artificial neural networks, the Adaptive Resonance Theory (ART) and the Kohonen network. The field data used as input for training represented monthly changes in density and species richness in selected taxa of benthic macroinvertebrates collected in the Suyong River in Korea from September 1993 to October 1994. The sampled data for each month was initially trained by ART, the weights of which preserved conformational characteristics among communities during the process of the training. Subsequently these weights were rearranged sequentially from 2 to 5 months, and were provided as input to the Kohonen network to reveal temporal variations in communities. The network was then able to extract the features of community dynamics in a reduced dimension covering the specified input period. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

Determining temporal pattern of community dynamics by using unsupervised learning algorithms

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Publisher
Elsevier
Copyright
Copyright © 2000 Elsevier Science B.V.
ISSN
0304-3800
eISSN
1872-7026
D.O.I.
10.1016/S0304-3800(00)00312-4
Publisher site
See Article on Publisher Site

Abstract

Analysis of patterns of temporal variation in community dynamics was conducted by combining two unsupervised artificial neural networks, the Adaptive Resonance Theory (ART) and the Kohonen network. The field data used as input for training represented monthly changes in density and species richness in selected taxa of benthic macroinvertebrates collected in the Suyong River in Korea from September 1993 to October 1994. The sampled data for each month was initially trained by ART, the weights of which preserved conformational characteristics among communities during the process of the training. Subsequently these weights were rearranged sequentially from 2 to 5 months, and were provided as input to the Kohonen network to reveal temporal variations in communities. The network was then able to extract the features of community dynamics in a reduced dimension covering the specified input period.

Journal

Ecological ModellingElsevier

Published: Jul 30, 2000

References

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