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.