366
M.F. Wilkins . L. Boddy . G.B.J. Dubelaar
References
Balfoort HW, Snoek J, Smits JRM, Breedveld LW, Hofstraat JW, Ringelberg J (1992)
Automatie identification of algae: neural network analysis of flow cytometric data. 1.
Plankton Res. 14, 575-589
Boddy L, Morris CW, Wilkins MF, Tarran GA, Burkill PH (1994) Neural network analysis
of flow cytometric data for 40 marine phytoplankton species. Cytometry 15,283-293
Boddy L, Morris CW, Wilkins MF, AI-Haddad L, Tarran GA, Jonker RR, Burkill PH
(2000) Identification of 72 phytoplankton species by radial basis function neural
network analysis offlow cytometric data. Mar. Ecol. Prog. Ser. 195,47-59
Burkill PH, Mantoura RFC (1990) The rapid analysis of single marine cells by flow
cytometry. Phil. Trans. Royal Soc. A333, 99-112
Chen S, Cowan CFN, Grant PM (1991) Orthogonal least-squares learning algorithm for
radial basis function neural networks. IEEE Trans. Neural Networks 2, 302-309
Dubelaar GBJ, Gerritzen PL, Beeker AER, Jonker RR, Tangen K (1999) Design and first
results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh
waters. Cytometry 137,247-254
Dubelaar GBJ, Gerritzen PL (2000) CytoBuoy: a step forward towards using flow
cytometry in Operation al Oceanography. Scientia Marina (in press)
Frankel DS, Olson RJ, Frankel SL, Chisholm SW (1989) Use of a neural net computer
system for analysis of flow cytometric data of phytoplankton populations. Cytometry
10,540-550
Frankel DS, Frankel SL, Binder BJ, Vogt RF (1996) Application ofneural networks to flow
cytometry data analysis and real-time cell classification. Cytometry 23, 290-302
Fu LM (1994) Neural Networks in Computer Intelligence. McGraw-Hill, New York
Godavarti M, Rodriguez 11, Yopp TA, Lambert GM, Galbraith DW (1996) Automated
particle classification based on digital acquisition and analysis of flow cytometric pulse
waveforms. Cytometry 24, 330-339
Haykin S (1994) Neural networks: a comprehensive foundation. Maxwell Macmillan
International, New York
Hush DR, Horne BG (1993) Progress in supervised neural networks- what's new since
Lippmann? IEEE Sig. Proc. Mag. 10,8-39
Joliffe IT (1986) Principal components analysis. Springer-Verlag, New York
Jonker RR, Meulemans JT, Dubelaar GBJ, Wilkins MF, Ringelberg J (1995) Flow
cytometry: a powerful tool in analysis of biomass distributions in phytoplankton.
Water Sei. Technol. 32, 177-182
Lippmann RP (1987) An introduction to computing with neural nets. IEEE Acoustics,
Speech and Signal Proc. Mag. 4, 4-22
Morris CW, Boddy L, Allman R (1992) Identification of basidiomycete spores by neural
network analysis of flow cytometry data. Mycol. Res. 96, 697-701
Morris CW, Boddy L (1996) Classification as unknown by RBF networks: discriminating
phytoplankton taxa fra m flow cytometry data. In: Dagli, C.H., Akay, M.,Chen, c.L.P.,
Fernandez, B.R., Ghosh, 1. (Editors), Intelligent Engineering Systems Through
Artificial Neural Networks, val. 6. ASME Press, New York, pp 629-634
Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1992) Numerical recipies in C: the
art of scientific computing. Cambridge University Press, 994 pp
M.F. Wilkins . L. Boddy . G.B.J. Dubelaar
References
Balfoort HW, Snoek J, Smits JRM, Breedveld LW, Hofstraat JW, Ringelberg J (1992)
Automatie identification of algae: neural network analysis of flow cytometric data. 1.
Plankton Res. 14, 575-589
Boddy L, Morris CW, Wilkins MF, Tarran GA, Burkill PH (1994) Neural network analysis
of flow cytometric data for 40 marine phytoplankton species. Cytometry 15,283-293
Boddy L, Morris CW, Wilkins MF, AI-Haddad L, Tarran GA, Jonker RR, Burkill PH
(2000) Identification of 72 phytoplankton species by radial basis function neural
network analysis offlow cytometric data. Mar. Ecol. Prog. Ser. 195,47-59
Burkill PH, Mantoura RFC (1990) The rapid analysis of single marine cells by flow
cytometry. Phil. Trans. Royal Soc. A333, 99-112
Chen S, Cowan CFN, Grant PM (1991) Orthogonal least-squares learning algorithm for
radial basis function neural networks. IEEE Trans. Neural Networks 2, 302-309
Dubelaar GBJ, Gerritzen PL, Beeker AER, Jonker RR, Tangen K (1999) Design and first
results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh
waters. Cytometry 137,247-254
Dubelaar GBJ, Gerritzen PL (2000) CytoBuoy: a step forward towards using flow
cytometry in Operation al Oceanography. Scientia Marina (in press)
Frankel DS, Olson RJ, Frankel SL, Chisholm SW (1989) Use of a neural net computer
system for analysis of flow cytometric data of phytoplankton populations. Cytometry
10,540-550
Frankel DS, Frankel SL, Binder BJ, Vogt RF (1996) Application ofneural networks to flow
cytometry data analysis and real-time cell classification. Cytometry 23, 290-302
Fu LM (1994) Neural Networks in Computer Intelligence. McGraw-Hill, New York
Godavarti M, Rodriguez 11, Yopp TA, Lambert GM, Galbraith DW (1996) Automated
particle classification based on digital acquisition and analysis of flow cytometric pulse
waveforms. Cytometry 24, 330-339
Haykin S (1994) Neural networks: a comprehensive foundation. Maxwell Macmillan
International, New York
Hush DR, Horne BG (1993) Progress in supervised neural networks- what's new since
Lippmann? IEEE Sig. Proc. Mag. 10,8-39
Joliffe IT (1986) Principal components analysis. Springer-Verlag, New York
Jonker RR, Meulemans JT, Dubelaar GBJ, Wilkins MF, Ringelberg J (1995) Flow
cytometry: a powerful tool in analysis of biomass distributions in phytoplankton.
Water Sei. Technol. 32, 177-182
Lippmann RP (1987) An introduction to computing with neural nets. IEEE Acoustics,
Speech and Signal Proc. Mag. 4, 4-22
Morris CW, Boddy L, Allman R (1992) Identification of basidiomycete spores by neural
network analysis of flow cytometry data. Mycol. Res. 96, 697-701
Morris CW, Boddy L (1996) Classification as unknown by RBF networks: discriminating
phytoplankton taxa fra m flow cytometry data. In: Dagli, C.H., Akay, M.,Chen, c.L.P.,
Fernandez, B.R., Ghosh, 1. (Editors), Intelligent Engineering Systems Through
Artificial Neural Networks, val. 6. ASME Press, New York, pp 629-634
Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1992) Numerical recipies in C: the
art of scientific computing. Cambridge University Press, 994 pp