By S. Lek, J. L. Giraudel, J. F. Guégan (auth.), Prof. Sovan Lek, Dr. Jean-François Guégan (eds.)
In this booklet, an simply comprehensible account of modelling equipment with synthetic neuronal networks for sensible functions in ecology and evolution is supplied. unique gains contain examples of functions utilizing either supervised and unsupervised education, comparative research of man-made neural networks and standard statistical equipment, and recommendations to accommodate bad datasets. vast references and a wide range of issues make this publication an invaluable advisor for ecologists, evolutionary ecologists and inhabitants geneticists.
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Extra info for Artificial Neuronal Networks
Setosa individuals residing mainly in the left lower plain, I. versicolor in the right upper plain and some I. virginica in a little plain area in the middle of the right side. The mountainous area from the upper left to the lower right part of the map mainly groups I. versicolor and I. virginica. Another interesting representation with SOM is the distribution of each variable on the map (Fig. 16). SOM is coloured for each component of weight vectors, namely Fig. 14. Iris data mapped on the organizing map.
References Ackley DH, Hinton GE, Sejnowski TJ (1985) A learning algorithm for Boltzmann machines. Cognitive science 9:147-169 Albiol J, Campmajo C, Casas C, Poch M (1995) Biomass estimation in plant cell cultures: A neuronal network approach. Biotechnology Progress 1l:88-92 Baran P, Lek S, Delacoste M, Belaud A (1996) Stochastic models that predict trout population densities or biomass on macrohabitat scale. Hydrobiologia 337:1-9 CHAPTER 1 • Neuronal Networks: Algorithms and Architectures for Ecologists 23 Bishop MC (1995) Neuronal networks for pattern recognition.
Aquatic Living Resources 9:23-29 Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J, Aulagnier S (1996b) Application of neuronal networks to modelling nonlinear relationships in ecology. Ecol Model 90:39-52 Lerner B, Levinstein M, Rosenberg B, Guterman H, Dinstein I, Romem Y (1994) Feature selection and chromosomes classification using a multilayer perceptron neuronal network. IEEE International conference on Neuronal Networks, Orlando (Florida), pp 3540-3545 Lo JY, Baker JA, Kornguth PJ, Floyd CE (1995) Application of artificial neuronal networks to interpretation of mammograms on the basis of the radiologists impression and optimized image features.