Identification of Phytoplankton from Flow Cytometry Data by Using Radial Basis Function Neural Networks
| Autor: | Lynne Boddy, R. R. Jonker, M. F. Wilkins, Colin W. Morris |
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| Přispěvatelé: | LAHBIB, SOUMAYA |
| Rok vydání: | 1999 |
| Předmět: |
Training set
Ecology Artificial neural network [SDU.OCEAN] Sciences of the Universe [physics]/Ocean Atmosphere Analyser Flow Cytometry Applied Microbiology and Biotechnology Gaussian radial basis function Identification (information) Radial basis function neural Phytoplankton Methods Neural Networks Computer Biological system ComputingMilieux_MISCELLANEOUS Analysis method Food Science Biotechnology |
| Zdroj: | Scopus-Elsevier |
| ISSN: | 1098-5336 0099-2240 |
| DOI: | 10.1128/aem.65.10.4404-4410.1999 |
| Popis: | We describe here the application of a type of artificial neural network, the Gaussian radial basis function (RBF) network, in the identification of a large number of phytoplankton strains from their 11-dimensional flow cytometric characteristics measured by the European Optical Plankton Analyser instrument. The effect of network parameters on optimization is examined. Optimized RBF networks recognized 34 species of marine and freshwater phytoplankton with 91.5% success overall. The relative importance of each measured parameter in discriminating these data and the behavior of RBF networks in response to data from “novel” species (species not present in the training data) were analyzed. |
| Databáze: | OpenAIRE |
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