Artificial Neuronal Netorks Application to Ecology and Evolution [Hardcover]

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.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.I Introduction.- 1 Neuronal Networks: Algorithms and Architectures for Ecologists and Evolutionary Ecologists.- 1.1 Introduction.- 1.2 Back Propagation Neuronal Network (BPN).- 1.2.1 Structure of BPN.- 1.2.2 BPN Algorithm.- 1.2.3 Training the Network.- 1.2.4 Testing the Network.- 1.2.5 Overtraining or Overfitting the Network.- 1.2.6 Use Aspects.- 1.2.7 BPN versus MLR.- 1.3 Kohonen Self-Organizing Mapping (SOM).- 1.3.1 Algorithm.- 1.3.2 Missing Data.- 1.3.3 Outliers.- 1.3.4 Use of Different Metrics.- 1.3.5 Aspects of Use.- 1.4 Conclusion.- Acknowledgements.- References.- II Artificial Neuronal Networks in Landscape Ecology and Remote Sensing.- 2 Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neuronal Networks.- 2.1 Introduction.- 2.2 Traditional Extraction Techniques.- 2.3 Neuronal Networks.- 2.4 Uses of Neuronal Networks and Remote Sensing Data.- 2l£‡