Lek S. auteur aut IRD Guégan Jean-François auteur aut IRD text journalArticle eng text/pdf reformatted digital access Artificial neural networks (ANNs) are non-linear mapping structures based on the function of the human brain. They have been shown to be universal and highly flexible function approximators for any data. These make powerful tools for models, especially when the underlying data relationships are unknown. For this reason, the international workshop on the applications of ANNs to ecological modelling was organized in Toulouse, France (December 1998). During this meeting, we discussed different methods, and their reliability to deal with ecological data. The special issue of this ecological modelling journal begins with the state-of-the-art with emphasis on the development of structural dynamic models presented by S.E. Jorgensen (DK). Then, to illustrate the ecological applications of ANNs, examples are drawn from several fields, e.g. terrestrial and aquatic ecosystems, remote sensing and evolutionary ecology. In this paper, we present some of the most important papers of the first workshop about ANNs in ecological modelling. We briefly introduce here two algorithms frequently used ; (i) one supervised network, the backpropagation algorithm ; and (ii) one unsupervised network, the Kohonen self-organizing mapping algorithm. The future development of ANNs is discussed in the present work. Several examples of modelling of ANNs in various areas of ecology are presented in this special issue. (Résumé d'auteur) specialized INTELLIGENCE ARTIFICIELLE MODELISATION ECOLOGIE METHODE D'ANALYSE ALGORITHME PREVISION 122INTAR 021ENVECO Lek S. ed. edt IRD Guégan Jean-François ed. edt IRD 120 2-3 65-73 1999 0304-3800 Applications of ANNs to Ecological Modelling : International Workshop, Toulouse (FRA), 1998/12 https://www.documentation.ird.fr/hor/fdi:010020707 10.1016/S0304-3800(99)00092-7 0304-3800 [F A010020706] https://www.documentation.ird.fr/hor/fdi:010020707 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2021-06/010020707.pdf IRD - Base Horizon / Pleins textes 2000-03-13 2021-06-15 fdi:010020707 fre