| Preface | p. vii |
| Introduction | p. 1 |
| Computational Ecology | p. 1 |
| Artificial Neural Networks and Ecological Applications | p. 3 |
| Artificial Neural Networks: Principles, Theories and Algorithms | p. 17 |
| Feedforward Neural Networks | p. 19 |
| Linear Separability and Perceptron | p. 20 |
| Some Analogies of Multilayer Feedforward Networks | p. 23 |
| Functionability of Multilayer Feedforward Networks | p. 23 |
| Linear Neural Networks | p. 25 |
| Linear Neural Networks | p. 25 |
| LMS Rule | p. 27 |
| Radial Basis Function Neural Networks | p. 29 |
| Theory of RBF Neural Network | p. 30 |
| Regularized RBF Neural Network | p. 31 |
| RBF Neural Network Learning | p. 33 |
| Probabilistic Neural Network | p. 34 |
| Generalized Regression Neural Network | p. 35 |
| Functional Link Neural Network | p. 35 |
| Wavelet Neural Network | p. 37 |
| BP Neural Network | p. 41 |
| BP Algorithm | p. 41 |
| BP Theorem | p. 44 |
| BP Training | p. 45 |
| Limitations and Improvements of BP Algorithm | p. 46 |
| Self-Organizing Neural Networks | p. 48 |
| Self-Organizing Feature Map Neural Network | p. 49 |
| Self-Organizing Competitive Learning Neural Network | p. 52 |
| Hamming Neural Network | p. 52 |
| WTA Neural Network | p. 53 |
| LVQ Neural Network | p. 54 |
| Adaptive Resonance Theory | p. 55 |
| Feedback Neural Networks | p. 58 |
| Elman Neural Network | p. 58 |
| Hopfield Neural Networks | p. 60 |
| Simulated Annealing | p. 62 |
| Boltzmann Machine | p. 63 |
| Design and Customization of Artificial Neural Networks | p. 67 |
| Mixture of Experts | p. 67 |
| Hierarchical Mixture of Experts | p. 69 |
| Neural Network Controller | p. 70 |
| Customization of Neural Networks | p. 72 |
| Learning Theory, Architecture Choice and Interpretability of Neural Networks | p. 76 |
| Learning Theory | p. 76 |
| Architecture Choice | p. 80 |
| Interpretability of Neural Networks | p. 82 |
| Mathematical Foundations of Artificial Neural Networks | p. 87 |
| Bayesian Methods | p. 87 |
| Randomization, Bootstrap and Monte Carlo Techniques | p. 90 |
| Stochastic Process and Stochastic Differential Equation | p. 96 |
| Interpolation | p. 100 |
| Function Approximation | p. 107 |
| Optimization Methods | p. 114 |
| Manifold and Differential Geometry | p. 115 |
| Functional Analysis | p. 122 |
| Algebraic Topology | p. 126 |
| Motion Stability | p. 126 |
| Entropy of a System | p. 130 |
| Distance or Similarity Measures | p. 132 |
| Matlab Neural Network Toolkit | p. 139 |
| Functions of Perceptron | p. 139 |
| Functions of Linear Neural Networks | p. 145 |
| Functions of BP Neural Network | p. 147 |
| Functions of Self-Organizing Neural Networks | p. 152 |
| Functions of Radial Basis Neural Networks | p. 157 |
| Functions of Probabilistic Neural Network | p. 158 |
| Function of Generalized Regression Neural Network | p. 159 |
| Functions of Hopfield Neural Network | p. 159 |
| Function of Elman Neural Network | p. 160 |
| Applications of Artificial Neural Networks in Ecology | p. 161 |
| Dynamic Modeling of Survival Process | p. 163 |
| Model Description | p. 164 |
| Data Description | p. 167 |
| Results | p. 167 |
| Discussion | p. 173 |
| Simulation of Plant Growth Process | p. 175 |
| Model Description | p. 175 |
| Data Source | p. 177 |
| Results | p. 177 |
| Discussion | p. 181 |
| Simulation of Food Intake Dynamics | p. 183 |
| Model Description | p. 183 |
| Data Description | p. 188 |
| Results | p. 188 |
| Discussion | p. 193 |
| Species Richness Estimation and Sampling Data Documentation | p. 194 |
| Estimation of Plant Species Richness on Grassland | p. 194 |
| Documentation of Sampling Data of Invertebrates | p. 204 |
| Modeling Arthropod Abundance from Plant Composition of Grassland Community | p. 213 |
| Model Description | p. 214 |
| Data Description | p. 217 |
| Results | p. 217 |
| Discussion | p. 222 |
| Pattern Recognition and Classification of Ecosystems and Functional Groups | p. 225 |
| Model Description | p. 226 |
| Data Source | p. 229 |
| Results | p. 230 |
| Discussion | p. 237 |
| Modeling Spatial Distribution of Arthropods | p. 238 |
| Model Description | p. 239 |
| Data Description | p. 245 |
| Results | p. 246 |
| Discussion | p. 253 |
| Risk Assessment of Species Invasion and Establishment | p. 256 |
| Invasion Risk Assessment Based on Species Assemblages | p. 257 |
| Determination of Abiotic Factors Influencing Species Invasion | p. 258 |
| Prediction of Surface Ozone | p. 260 |
| BP Prediction of Daily Total Ozone | p. 261 |
| MLP Prediction of Hourly Ozone Levels | p. 262 |
| Modeling Dispersion and Distribution of Oxide and Nitrate Pollutants | p. 264 |
| Modeling Nitrogen Dioxide Dispersion | p. 265 |
| Simulation of Nitrate Distribution in Ground Water | p. 266 |
| Modeling Terrestrial Biomass | p. 268 |
| Estimation of Aboveground Grassland Biomass | p. 268 |
| Estimation of Trout Biomass | p. 269 |
| References | p. 271 |
| Index | p. 289 |
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