Computational Ecology: Artificial Neural Networks And Their Applications

by Wenjun Zhang

Overview

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.

Product Details

ISBN-13:	9789814282628
Publisher:	World Scientific Publishing Company, Incorporated
Publication date:	06/28/2010
Pages:	312
Product dimensions:	6.10(w) x 9.10(h) x 0.80(d)

Table of Contents

Preface vii

Chapter 1 Introduction 1

1 Computational Ecology 1

2 Artificial Neural Networks and Ecological Applications 3

Part I Artificial Neural Networks: Principles, Theories and Algorithms 17

Chapter 2 Feedforward Neural Networks 19

1 Linear Separability and Perceptron 20

2 Some Analogies of Multilayer Feedforward Networks 23

3 Functionability of Multilayer Feedforward Networks 23

Chapter 3 Linear Neural Networks 25

1 Linear Neural Networks 25

2 LMS Rule 27

Chapter 4 Radial Basis Function Neural Networks 29

1 Theory of RBF Neural Network 30

2 Regularized RBF Neural Network 31

3 RBF Neural Network Learning 33

4 Probabilistic Neural Network 34

5 Generalized Regression Neural Network 35

6 Functional Link Neural Network 35

7 Wavelet Neural Network 37

Chapter 5 BP Neural Network 41

1 BP Algorithm 41

2 BP Theorem 44

3 BP Training 45

4 Limitations and Improvements of BP Algorithm 46

Chapter 6 Self-Organizing Neural Networks 48

1 Self-Organizing Feature Map Neural Network 49

2 Self-Organizing Competitive Learning Neural Network 52

3 Hamming Neural Network 52

4 WTA Neural Network 53

5 LVQ Neural Network 54

6 Adaptive Resonance Theory 55

Chapter 7 Feedback Neural Networks 58

1 Elman Neural Network 58

2 Hopfield Neural Networks 60

3 Simulated Annealing 62

4 Boltzmann Machine 63

Chapter 8 Design and Customization of Artificial Neural Networks 67

1 Mixture of Experts 67

2 Hierarchical Mixture of Experts 69

3 Neural Network Controller 70

4 Customization of Neural Networks 72

Chapter 9 Learning Theory, Architecture Choice and Interpretability of Neural Networks 76

1 Learning Theory 76

2 Architecture Choice 80

3 Interpretability of Neural Networks 82

Chapter 10 Mathematical Foundations of Artificial Neural Networks 87

1 Bayesian Methods 87

2 Randomization, Bootstrap and Monte Carlo Techniques 90

3 Stochastic Process and Stochastic Differential Equation 96

4 Interpolation 100

5 Function Approximation 107

6 Optimization Methods 114

7 Manifold and Differential Geometry 115

8 Functional Analysis 122

9 Algebraic Topology 126

10 Motion Stability 126

11 Entropy of a System 130

12 Distance or Similarity Measures 132

Chapter 11 Matlab Neural Network Toolkit 139

1 Functions of Perceptron 139

2 Functions of Linear Neural Networks 145

3 Functions of BP Neural Network 147

4 Functions of Self-Organizing Neural Networks 152

5 Functions of Radial Basis Neural Networks 157

6 Functions of Probabilistic Neural Network 158

7 Function of Generalized Regression Neural Network 159

8 Functions of Hopfield Neural Network 159

9 Function of Elman Neural Network 160

Part II Applications of Artificial Neural Networks in Ecology 161

Chapter 12 Dynamic Modeling of Survival Process 163

1 Model Description 164

2 Data Description 167

3 Results 167

4 Discussion 173

Chapter 13 Simulation of Plant Growth Process 175

1 Model Description 175

2 Data Source 177

3 Results 177

4 Discussion 181

Chapter 14 Simulation of Food Intake Dynamics 183

1 Model Description 183

2 Data Description 188

3 Results 188

4 Discussion 193

Chapter 15 Species Richness Estimation and Sampling Data Documentation 194

1 Estimation of Plant Species Richness on Grassland 194

2 Documentation of Sampling Data of Invertebrates 204

Chapter 16 Modeling Arthropod Abundance from Plant Composition of Grassland Community 213

1 Model Description 214

2 Data Description 217

3 Results 217

4 Discussion 222

Chapter 17 Pattern Recognition and Classification of Ecosystems and Functional Groups 225

1 Model Description 226

2 Data Source 229

3 Results 230

4 Discussion 237

Chapter 18 Modeling Spatial Distribution of Arthropods 238

1 Model Description 239

2 Data Description 245

3 Results 246

4 Discussion 253

Chapter 19 Risk Assessment of Species Invasion and Establishment 256

1 Invasion Risk Assessment Based on Species Assemblages 257

2 Determination of Abiotic Factors Influencing Species Invasion 258

Chapter 20 Prediction of Surface Ozone 260

1 BP Prediction of Daily Total Ozone 261

2 MLP Prediction of Hourly Ozone Levels 262

Chapter 21 Modeling Dispersion and Distribution of Oxide and Nitrate Pollutants 264

1 Modeling Nitrogen Dioxide Dispersion 265

2 Simulation of Nitrate Distribution in Ground Water 266

Chapter 22 Modeling Terrestrial Biomass 268

1 Estimation of Aboveground Grassland Biomass 268

2 Estimation of Trout Biomass 269

References 271

Index 289