Computational Ecology: Artificial Neural Networks and Their Applications
Due to the complexity and non-linearity of most ecological problems, artificial neural networks (ANNs) have attracted attention from ecologists and environmental scientists in recent years. As these networks are increasingly being used in ecology for modeling, simulation, function approximation, prediction, classification and data mining, this unique and self-contained book will be the first comprehensive treatment of this subject, by providing readers with overall and in-depth knowledge on algorithms, programs, and applications of ANNs in ecology. Moreover, a new area of ecology, i.e., computational ecology, is proposed and its scopes and objectives are defined and discussed. Computational Ecology consists of two parts: the first describes the methods and algorithms of ANNs, interpretability and mathematical generalization of neural networks, Matlab neural network toolkit, etc., while the second provides case studies of applications of ANNs in ecology, Matlab codes, and comparisons of ANNs with conventional methods.This publication will be a valuable reference for research scientists, university teachers, graduate students and high-level undergraduates in the areas of ecology, environmental sciences, and computational science.
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analysis architecture arthropod abundance arthropods artificial neural networks backpropagation basis functions Between-layer weights bias Boltzmann machine Bootstrap BP network BP neural network classification coefficient computational ecology connection weights Cross validation data set default differential equation distribution of arthropods Elman network epochs estimate Euclidean distance Fecit feedforward network FLANN grassland hidden layer hidden neurons Hopfield input layer input space input variables input vectors Input weights insect interpolation invertebrate learning function learning rate linear discriminant analysis linear neural network manifold mapping Mathworks Matlab method multivariate network output neural network models neurons nodes nonlinear function number of hidden number of neurons output layer parameters pattern perceptron polynomial prediction probability distribution quadrats random numbers regression response surface model s x q self-organizing self-organizing map simulation performance spatial distribution species richness spline Suppose survival process Syntax tansig topological function training samples transfer function values VC dimension yield