Computational ecology : artificial neural networks and their applications / Wenjun Zhang. — New Jersey ; London : World Scientific, c2010. – (58.18056/Z63)

Contents

    Contents
    
    Preface
    Chapter l. Introduction
    1. Computational Ecology
    2. Artificial Neural Networks and Ecological Applications
    Part I Artificial Neural Networks: Principles， Theories and Algorithms
    Chapter 2. Feedforward Neural Networks
    1. Linear Separability and Perceptron
    2. Some Analogies of Multilayer Feedforward Networks
    3. Functionability of Multilayer Feedforward Networks
    Chapter 3. Linear Neural Networks
    1. Linear Neural Networks
    2. LMS Rule
    Chapter 4. Radial Basis Function Neural Networks
    1. Theory of RBF Neural Network
    2. Regularized RBF Neural Network
    3. RBF Neural Network Learning
    4. Probabilistic Neural Network
    5. Generalized Regression Neural Network
    6. Functional Link Neural Network 35
    7. Wavelet Neural Network 37
    Chapter 5. BP Neural Network
    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
    1. Self-Organizing Feature Map Neural Network
    2. Self-Organizing Competitive Learning Neural Network
    3. Hamming Neural Network
    4. WTA Neural Network
    5. LVQ Neural Network
    6. Adaptive Resonance Theory
    Chapter 7. Feedback Neural Networks
    I. 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
    1. Mixture of Experts
    2. Hierarchical Mixture of Experts
    3. Neural Network Controller
    4. Customization of neural networks
    Chapter 9. Learning theory， architecture choice and interpretability of neural networks
    1. Learning theory
    2. Architecture choice
    3. Interpretability of Neural Networks
    Chapter 10. Mathematical Foundations of Artificial Neural Networks
    1. Bayesian Methods
    2. Randomization， Bootstrap and Monte Carlo Techniques
    3. Stochastic Process and Stochastic Differential Equation
    4. Interpolation
    5. Function Approximation
    6. Optimization Methods
    7. Manifold and Differential Geometry
    8. Functional Analysis
    9. Algebraic Topology
    10. Motion Stability
    11. Entropy of a System
    12. Distance or Similarity Measures
    Chapter 11. Matlab Neural Network Toolkit
    1. Functions of Perceptron
    2. Functions of Linear Neural Networks
    3. Functions of BP Neural Network
    4. Functions of Self-Organizing Neural Networks
    5. Functions of Radial Basis Neural Networks
    6. Functions of Probabilistic Neural Network
    7. Function of Generalized Regression Neural Network
    8. Functions of Hopfield Neural Network
    9. Function of Elman Neural Network
    Part II Applications of Artificial Neural Networks in Ecology
    Chapter 12. Dynamic Modeling of Survival Process
    1. Model Description
    2. Data Description
    3. Results
    4. Discussion
    Chapter 13. Simulation of Plant Growth Process
    1. Model Description
    2. Data Source
    3. Results
    4. Discussion
    Chapter 14. Simulation of Food Intake Dynamics
    1. Model Description
    2. Data Description
    3. Results
    4. Discussion
    Chapter 15. Species Richness Estimation and Sampling Data Documentation
    1. Estimation of Plant Species Richness on Grassland
    2. Documentation of Sampling Data of Invertebrates
    Chapter 16. Modeling Arthropod Abundance from Plant Composition of Grassland Community
    1. Model Description
    2. Data Description
    3. Results
    4. Discussion
    Chapter 17. Pattern Recognition and Classification of Ecosystems and Functional Groups
    1. Model Description
    2. Data Source
    3. Results
    4. Discussion
    Chapter 18. Modeling Spatial Distribution of Arthropods
    1. Model Description
    2. Data Description
    3. Results
    4. Discussion
    Chapter 19. Risk Assessment of Species Invasion and Establishment
    1. Invasion Risk Assessment Based on Species Assemblages
    2. Determination of Abiotic Factors Influencing Species Invasion
    Chapter 20. Prediction of Surface Ozone
    1. BP Prediction of Daily Total Ozone
    2. MLP Prediction of Hourly Ozone Levels
    Chapter 21. Modeling Dispersion and Distribution of Oxide and Nitrate Pollutants
    1. Modeling Nitrogen Dioxide Dispersion
    2. Simulation of Nitrate Distribution in Ground Water
    Chapter 22. Modeling Terrestrial Biomass
    1. Estimation of Aboveground Grassland Biomass
    2. Estimation of Trout Biomass
    References
    Index