Computer-Intensive Methods in Control and Signal Processing

Fighting dimensionality with linguistic geometry, Boris Stilman; Statistical physics and the optimization of autonomous behaviour in complex virtual worlds, Robert W. Penney; On merging gradient estimation with mean-tracking techniques for cluster identification, Paul D. Fox et al; Computational aspects of graph theoretic methods in control, Katalin M. Hangos, Zsolt Tuza; Efficient algorithms for predictive control of systems with bounded inputs, Luigi Chisci et al; Applying new numerical algorithms to the solution of discrete-time optimal control problems, Rudiger Franke, Eckhard Arnold; System identification using composition networks, Yves Moreau, Joos Vandewalle; Recursive nonlinear estimation of non-linear/non-Gaussian dynamic models, Rudolf Kulhavy; Monte Carlo approach to Bayesian regression modelling, Jan Smid et al; Identification of reality in Bayesian context, Ludek Berec, Miroslav Karny; Nonlinear nonnormal dynamic models - state estimation and software, Miroslav Simandl, Miroslav Flidr; The EM algorithm - a guided tour, Christophe Couvreur; estimation of quasipolynomilas in noise - theoretical algorithmic and implementation aspects, Vytautas Slivinskas, Virginija Simonyte; Iterative reconstruction of transmission sinograms with low signal to noise ratio, Johan Nuyts et al; Curse of dimensionality - classifying large multi-dimensional images with neural networks, Rudolf Hanka, Thomas P. Harte; Dimension-independent rates of approximation by neural networks, Vera Kurkova; estimation of human signal detection performance from event-related potentials using feed-forward neural network model, Milos Koska et al; Utilizing geometric anomalies of high dimension - when complexity makes computation easier, Paul C. Kainen; Approximation using cubic B-splines with improved training speed and accuracy, Julian D. Mason et al.