Book Description

Ch. 1. Introduction. 1. Computational ecology. 2. Artificial neural networks and ecological applications -- pt. I. Artificial neural networks : principles, theories and algorithms. ch. 2. Feedforward neural networks. 1. Linear separability and perceptron. 2. Some analogies of multilayer feedforward networks. 3. Functionability of multilayer feedforward networks. ch. 3. Linear neural networks. 1. Linear neural networks. 2. LMS rule. ch. 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. 7. Wavelet neural network. ch. 5. BP neural network. 1. BP algorithm. 2. BP theorem. 3. BP training. 4. Limitations and improvements of BP algorithm. ch. 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. ch. 7. Feedback neural networks. 1. Elman neural network. 2. Hopfield neural networks. 3. Simulated annealing. 4. Boltzmann machine. ch. 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. ch. 9. Learning theory, architecture choice and interpretability of neural networks. 1. Learning theory. 2. Architecture choice. 3. Interpretability of neural networks. ch. 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. ch. 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 -- pt. II. Applications of artificial neural networks in ecology. ch. 12. Dynamic modeling of survival process. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 13. Simulation of plant growth process. 1. Model description. 2. Data source. 3. Results. 4. Discussion. ch. 14. Simulation of food intake dynamics. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 15. Species richness estimation and sampling data documentation. 1. Estimation of plant species richness on grassland. 2. Documentation of sampling data of invertebrates. ch. 16. Modeling arthropod abundance from plant composition of grassland community. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 17. Pattern recognition and classification of ecosystems and functional groups. 1. Model description. 2. Data source. 3. Results. 4. Discussion. ch. 18. Modeling spatial distribution of arthropods. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 19. Risk assessment of species invasion and establishment. 1. Invasion risk assessment based on species assemblages. 2. Determination of abiotic factors influencing species invasion. ch. 20. Prediction of surface ozone. 1. BP prediction of daily total ozone. 2. MLP Prediction of hourly ozone levels. ch. 21. Modeling dispersion and distribution of oxide and nitrate pollutants. 1. Modeling nitrogen dioxide dispersion. 2. Simulation of nitrate distribution in ground water. ch. 22. Modeling terrestrial biomass. 1. Estimation of aboveground grassland biomass. 2. Estimation of trout biomass