By S. Lek, J. L. Giraudel, J. F. Guégan (auth.), Prof. Sovan Lek, Dr. Jean-François Guégan (eds.)
In this ebook, an simply comprehensible account of modelling equipment with man made neuronal networks for useful functions in ecology and evolution is equipped. exact good points contain examples of functions utilizing either supervised and unsupervised education, comparative research of synthetic neural networks and standard statistical equipment, and suggestions to house bad datasets. wide references and a wide variety of subject matters make this publication an invaluable advisor for ecologists, evolutionary ecologists and inhabitants geneticists.
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These are called "local minima" which correspond to a partial solution for the network in response to the training data. Ideally, we seek a global minimum (lowest error value possible); nevertheless, the local minima are surrounded and the network usually does not leave it by the standard BPN algorithm. Special techniques should be used to get out of a local minimum: changing the learning parameter or the number of hidden units, but notably by the use of momentum term (ex) in the algorithm. The momentum term is chosen generally between 0 and 1.
9). The synaptic weights are high and constant for W = variable. Using one hidden layer, we improved the quality of prediction (Fig. 10): practically all observations are aligned on the perfect line (coordinate 1: 1). For more details, see Lek et al. (1995). 1 Algorithm The Kohonen SOM falls into the category of unsupervised competitive learning (Fig. 11) methodology, in which the relevant multivariate algorithms seek clusters in the data (Everitt 1993). Conventionally, at least in ecology, the reduction of the multivariate data is usually carried out using principal components analysis or hierarchical clustering analysis (Jongman et al.
B after log transformation of some of the variables: 10g(Q! 477P This model is built with 8 independent variables: the asymptotic weight of the species (W=), the morphological ratio A representing the motor activity of the fish, the mean annual temperature (T), three discrete variables defining the diet, herbivorous (h = 1), detritivorous (d = 1), farmed fish (p = 1) and carnivorous (h = d =P = 0), and two morphological measurements: D = standard length! height of the body and P = height of the tail!