Artificial Neuronal Networks

  • Sovan Lek
  • Jean-François Guégan

Part of the Environmental Science book series (ESE)

Table of contents

  1. Front Matter
    Pages I-XXVI
  2. Introduction

    1. Front Matter
      Pages 1-1
  3. Artificial Neuronal Networks in Landscape Ecology and Remote Sensing

  4. Artificial Neuronal Networks in Population, Community and Ecosystem Ecology

  5. Artificial Neuronal Networks in Genetics and Evolutionary Ecology

  6. Perspectives

    1. Front Matter
      Pages 239-239
    2. W. Silvert, M. Baptist
      Pages 241-248
  7. Back Matter
    Pages 249-262

About this book


In this book, an easily understandable account of modelling methods with artificial neuronal networks for practical applications in ecology and evolution is provided. Special features include examples of applications using both supervised and unsupervised training, comparative analysis of artificial neural networks and conventional statistical methods, and proposals to deal with poor datasets. Extensive references and a large range of topics make this book a useful guide for ecologists, evolutionary ecologists and population geneticists.


Tempo algorithms artificial neural network ecology ecosystem ecosystem ecology evolution fuzzy genetics modeling neural networks performance phytoplankton plankton vegetation

Editors and affiliations

  • Sovan Lek
    • 1
  • Jean-François Guégan
    • 2
  1. 1.C.E.S.A.C., U.M.R. C.N.R.S. 5576 C Bâtiment IVR3Université Paul Sabatier Toulouse IIIToulouse cedex 04France
  2. 2.Centre d’Etudes sur le Polymorphisme des Micro-organismes C.E.P.M/U.M.R C.N.R.S.-I.R.D. 9926ORSTOMMontpellier cedex 01France

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2000
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-63116-0
  • Online ISBN 978-3-642-57030-8
  • Series Print ISSN 1863-5520
  • Buy this book on publisher's site