126
I.M. Schleiter . M. Obach . R. Wagner· H. Wemer· H.-H. Schmidt
. D. Borchardt
Obach M (1998) Anwendung Statistischer Methoden und Künstlicher Neuronaler
Netzwerke im Vergleich. Diplomarbeit im Fachbereich Mathematik/Informatik der
Universität Gesamthochschule Kassel (unpubl.)
Obach M, Wagner R, Wemer H, Schmidt HH (2001) Modelling population dynamics of
aquatic insects with Artificial Neural Networks. Ecol. Modell. 146, 1-3,207-217.
Resh VH, Hildrew AG, Statzner B, Townsend CR (1994) Theoretical habitat templets,
species traits and species richness: a synthesis of long-term ecological research on the
Upper Rhöne River in the context of concurrently developed ecological theory.
Freshw. Biol. 31, 539--554
Ritter H, Martinetz T, Schulten K (1992) Neuronale Netze (2. ed.), Addison-Wesley, Bonn
Rumelhart DE, Hinton GE, WiIliams RJ (1986) Leaming internal representations by error
propagation. In: Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing
(Vol. I), MIT Press, pp. 318ff
Sammon JW (1969) A nonlinear mapping for data structure analysis. IEEE Trans. Comput.
C-185,401--409
Schleiter IM, Borchardt D, Wagner R, Dapper T, Schmidt KD, Schmidt HH, Werner H
(1999) Modelling water quality, bioindication and population dynamics in lotic
ecosystems using neural networks. Ecol. Modell. 120,271--286
Schleiter IM, Obach M, Borchardt D, Werner H (2001) Prediction of running water
properties using Radial Basis Function Self-Organising Maps combined with input
relevance detection (i.prep.)
Schleiter IM, Obach M, Borchardt D, Werner H (2001) Bioindication of chemical and
hydro-morphological habitat characteristics with benthic macro-invertebrates based on
artificial neural networks, Aquat. Ecol. 35, 147--158
Specht DF (1991) A general regression network. In: IEEE Transactions on Neural
Networks 6, 568--576
Statzner B, Resh VH, Doledec S (Eds.) (1994) Ecology of the Upper Rhöne River: a test of
habitat templet theories. Special issue. Freshw. Biol. 31 (3) pp. 556
Townsend CR (1989) The patch dynamics concept of stream community ecology. J. N.
Am. Benthol. Soc. 8, 36--50
Townsend CR, Hildrew AG (1994) Species traits in relation to a habitat templet for river
systems. Freshw. Biol. 31, 265--275
Ultsch A (1993) Self organized feature maps for monitoring and knowledge acquisition of a
chemical process. In: Gielen, S., Kappen, B. (Eds.), Proceedings of the International
Conference on Artificial Neural Networks ICANN 93,864-867, Springer, London
Vannote RL, Minshall GW, Cumrnins KW, Sedell JR, Cushing CE (1980) The river
continuum concept. Can. 1. Fish. Aquat. Sci. 37, 130--137
Wagner R, Dapper T, Schmidt HH (2000): The influence of environmental variables on the
abundance of aquatic insects: a comparison of ordination and artificial neural
networks. Hydrobiologia 422/423, 143--152
Werner H, Obach M (2001) New neural network types estimating the accuracy ofresponse
for ecological modelling. Ecol. Modell. 146, 1-3,289-298.
I.M. Schleiter . M. Obach . R. Wagner· H. Wemer· H.-H. Schmidt
. D. Borchardt
Obach M (1998) Anwendung Statistischer Methoden und Künstlicher Neuronaler
Netzwerke im Vergleich. Diplomarbeit im Fachbereich Mathematik/Informatik der
Universität Gesamthochschule Kassel (unpubl.)
Obach M, Wagner R, Wemer H, Schmidt HH (2001) Modelling population dynamics of
aquatic insects with Artificial Neural Networks. Ecol. Modell. 146, 1-3,207-217.
Resh VH, Hildrew AG, Statzner B, Townsend CR (1994) Theoretical habitat templets,
species traits and species richness: a synthesis of long-term ecological research on the
Upper Rhöne River in the context of concurrently developed ecological theory.
Freshw. Biol. 31, 539--554
Ritter H, Martinetz T, Schulten K (1992) Neuronale Netze (2. ed.), Addison-Wesley, Bonn
Rumelhart DE, Hinton GE, WiIliams RJ (1986) Leaming internal representations by error
propagation. In: Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing
(Vol. I), MIT Press, pp. 318ff
Sammon JW (1969) A nonlinear mapping for data structure analysis. IEEE Trans. Comput.
C-185,401--409
Schleiter IM, Borchardt D, Wagner R, Dapper T, Schmidt KD, Schmidt HH, Werner H
(1999) Modelling water quality, bioindication and population dynamics in lotic
ecosystems using neural networks. Ecol. Modell. 120,271--286
Schleiter IM, Obach M, Borchardt D, Werner H (2001) Prediction of running water
properties using Radial Basis Function Self-Organising Maps combined with input
relevance detection (i.prep.)
Schleiter IM, Obach M, Borchardt D, Werner H (2001) Bioindication of chemical and
hydro-morphological habitat characteristics with benthic macro-invertebrates based on
artificial neural networks, Aquat. Ecol. 35, 147--158
Specht DF (1991) A general regression network. In: IEEE Transactions on Neural
Networks 6, 568--576
Statzner B, Resh VH, Doledec S (Eds.) (1994) Ecology of the Upper Rhöne River: a test of
habitat templet theories. Special issue. Freshw. Biol. 31 (3) pp. 556
Townsend CR (1989) The patch dynamics concept of stream community ecology. J. N.
Am. Benthol. Soc. 8, 36--50
Townsend CR, Hildrew AG (1994) Species traits in relation to a habitat templet for river
systems. Freshw. Biol. 31, 265--275
Ultsch A (1993) Self organized feature maps for monitoring and knowledge acquisition of a
chemical process. In: Gielen, S., Kappen, B. (Eds.), Proceedings of the International
Conference on Artificial Neural Networks ICANN 93,864-867, Springer, London
Vannote RL, Minshall GW, Cumrnins KW, Sedell JR, Cushing CE (1980) The river
continuum concept. Can. 1. Fish. Aquat. Sci. 37, 130--137
Wagner R, Dapper T, Schmidt HH (2000): The influence of environmental variables on the
abundance of aquatic insects: a comparison of ordination and artificial neural
networks. Hydrobiologia 422/423, 143--152
Werner H, Obach M (2001) New neural network types estimating the accuracy ofresponse
for ecological modelling. Ecol. Modell. 146, 1-3,289-298.