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 tools with man made neuronal networks for useful purposes in ecology and evolution is equipped. detailed beneficial properties contain examples of functions utilizing either supervised and unsupervised education, comparative research of man-made neural networks and standard statistical equipment, and suggestions to accommodate negative datasets. huge references and a wide range of issues make this booklet an invaluable consultant for ecologists, evolutionary ecologists and inhabitants geneticists.
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Additional resources for Artificial Neuronal Networks
E. containing few examples of data. The BPN may now be trained with n - 1 parts, and tested with the last part. The same network struc- CHAPTER 1 • Neuronal Networks: Algorithms and Architectures for Ecologists 11 ture may be repeated to use every test set once in one of the n procedures. The result of these tests together provide the performance of the model. Sometimes, in extreme cases, the test set can have only one example, and this is called the leave-one-out procedure (Efron 1983; Kohavi 1995).
Ideally, in the scientific community, one would like to develop accurate, physicallybased models for the physical system being studied. This model serves as a hypothesis for our current understanding of the physical system and as a basis for extracting desired vegetation variables from other readily known/measured variables. These physically-based models are forced to address the entire radiative transfer problem which includes a large number of variables. In remote sensing applications many of these variables are not of interest.
To actually use physically-based models for extracting vegetation variables, the models must be inverted. In most cases these models are complex nonlinear systems which must be solved using numerical methods. The traditional approach employs numerical optimization techniques that, once initialised, search for the optimum parameter set that minimizes the error. There are difficulties in using these techniques. A stable and optimum inversion is not guaranteed and the technique can be computationally intensive when using complex radiative transfer models of vegetation.