Computer Intensive Methods in Control and Signal Processing

1. Fighting Dimensionality with Linguistic Geometry. - 2. Statistical Physics and the Optimization of Autonomous Behaviour in Complex Virtual Worlds. - 3. On Merging Gradient Estimation with Mean-Tracking Techniques for Cluster Identification. - 4. Computational Aspects of Graph Theoretic Methods in Control. - 5. Efficient Algorithms for Predictive Control of Systems with Bounded Inputs. - 6. Applying New Numerical Algorithms to the Solution of Discrete-time Optimal Control Problems. - 7. System Identification using Composition Networks. - 8. Recursive Nonlinear Estimation of Non-linear/Non-Gaussian Dynamic Models. - 9. Monte Carlo Approach to Bayesian Regression Modelling. - 10. Identification of Reality in Bayesian Context. - 11. Nonlinear Nonnormal Dynamic Models: State Estimation and Software. - 12. The EM Algorithm: A Guided Tour. - 13. Estimation of Quasipolynomials in Noise: Theoretical Algorithmic and Implementation Aspects. - 14. Iterative Reconstruction of Transmission Sinograms with Low Signal to Noise Ratio. - 15. Curse of Dimensionality: Classifying Large Multi-Dimensional Images with Neural Networks. - 16. Dimension-independent Rates of Approximation by Neural Networks. - 17. Estimation of Human Signal Detection Performance from Event-Related Potentials Using Feed-Forward Neural Network Model. - 18. Utilizing Geometric Anomalies of High Dimension: When Complexity Makes Computation Easier. - 19. Approximation Using Cubic B-Splines with Improved Training Speed and Accuracy. Language: English