Partitioned RBF networks for identification of biological taxa: discrimination of phytoplankton from flow cytometry data

Intelligent Engineering Systems Through Artificial Neural Networks

Volumn 1998, Issue , 1998, Pages 637-642

  Morris, Colin W ^a   Boddy, Lynne ^b  

^a UNIVERSITY OF GLAMORGAN   (United Kingdom)

^b CARDIFF UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGAE; FUNCTIONS; IDENTIFICATION (CONTROL SYSTEMS); LEARNING SYSTEMS;

FLOW CYTOMETRY; PHYTOPLANKTON; RADIAL BASIS FUNCTION; TAXA;

NEURAL NETWORKS;

EID: 0032289288     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Article

References (12)

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2. Boddy, L., Morris, C.W., Wilkins, M.F., Tarran, G.A. and Burkill, P.H. (1994b). Neural network analysis of flow cytometric data for 40 marine phytoplankton species, Cytometry 15, 283-293.
3. Chen, J., Atalan, E., Ward, A.C. and Goodfellow, M. (1993). Artificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains. FEMS Microbiology Letters 107, 321 - 326.
4. Frankel., D.S., Frankel, S.L., Binder, B.J. and Vogt, R.F. (1996). Application of neural networks to flow cytometry data analysis and real-time cell classification. Cytometry 23, 290 - 302.
5. Goodacre, R. Neal, M.R., Kell, D.B., Greenham, L.W., Noble, W.C. and Harvey, R.G. (1994). Rapid identification using pyrolysis mass spectrometry and artificial neural networks of Propionibacterium acnes isolated from dogs. Journal of Applied Bacteriology 13, 157 - 160.
6. Goodacre, R., Hiom, S.J., Cheeseman, S.L., Murdoch, D., Weightman, A.J. and Wade, W.G. (1996). Identification and discrimination of oral asaccharolytic Eubacterium spp. by pyrolysis mass spectrometry and artificial neural networks. Current Microbiology 32, 77-84.
7. Morgan, A., Boddy, L., Morris, C.W. and Mordue, J.E.M. (1998). Identification of species in the genus Pestalotiopsis from spore morphometric data: a comparison of some neural and non-neural methods. Mycological Research, (in press)
8. Morris, C.W. and Boddy, L. (1996). Classification as unknown by RBF networks: discriminating phytoplankton taxa from flow cytometry data. In: Intelligent Engineering Systems Through Artificial Neural Networks, Vol. 6. (ed. C.H. Dagli, M. Akay, C.L.P. Chen, B.R. Fernandez and J. Ghosh). ASME Press, New York, pp. 629 - 634.
9. Morris, C. W., Boddy, L., and Allman, R. (1992). Identification of basidiomycete spores by neural network analysis of flow cytometry data. Mycological Research, 96, 697-701.
10. Morris, C.W., Boddy, L. and Wilkins, M.F. (1994). Approaches to applying neural networks to the identification of phytoplankton taxa from flow cytometry data. In: C.H. Dagli, B.R. Fernandez, J. Ghosh and R.T.S. Kumara (ed.) Intelligent Engineering Systems Through Artificial Neural Networks, Vol. 4. ASME Press, New York, pp. 619 - 627.
11. Wilkins, M.F., Boddy, L., Morris, C.W. and Jonker, R. (1996). A comparison of some neural and non-neural methods for identification of phytoplankton from flow cytometry data. CABIOS 12, 9 - 18.
12. Wilkins, M.F., Morris, C.W. and Boddy, L. (1994). A comparison of radial basis function and backpropagation neural networks for identification of marine phytoplankton from multivariate flow cytometry data. CABIOS 10, 285-294.