Computer-intensive methods in control and signal processing : the curse of dimensionality

著者

書誌事項

Computer-intensive methods in control and signal processing : the curse of dimensionality

Kevin Warwick, Miroslav Kárný, editors

Birkhäuser, 1997

  • : Boston
  • : Basel

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注記

"2nd European IEEE Workshop on Computer-Intensive Methods in Control and Signal Processing: Can We Beat the Curse of Dimensionality? Prague, Czech Republic, August 28-30, 1996"--P. [xi]

Includes bibliographical references

内容説明・目次

巻冊次

: Boston ISBN 9780817639891

内容説明

Due to the rapid increase in readily available computing power, a corre sponding increase in the complexity of problems being tackled has occurred in the field of systems as a whole. A plethora of new methods which can be used on the problems has also arisen with a constant desire to deal with more and more difficult applications. Unfortunately by increasing the ac curacy in models employed along with the use of appropriate algorithms with related features, the resultant necessary computations can often be of very high dimension. This brings with it a whole new breed of problem which has come to be known as "The Curse of Dimensionality" . The expression "Curse of Dimensionality" can be in fact traced back to Richard Bellman in the 1960's. However, it is only in the last few years that it has taken on a widespread practical significance although the term di mensionality does not have a unique precise meaning and is being used in a slightly different way in the context of algorithmic and stochastic complex ity theory or in every day engineering. In principle the dimensionality of a problem depends on three factors: on the engineering system (subject), on the concrete task to be solved and on the available resources. A system is of high dimension if it contains a lot of elements/variables and/or the rela tionship/connection between the elements/variables is complicated.

目次

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.
巻冊次

: Basel ISBN 9783764339890

内容説明

This volume is based upon the 2nd IEEE European Workshop on Computer-Intensive Methods in Control and Signal Processing, subtitled "Can We Beat the Curse of Dimensionality?", held in Prague, August 1996. The book brings together a blend of approaches to a common theme from a variety of international research groups in engineering. The 18 contributions which make up this text originated as selected papers from the 1996 workshop, and have been modified and edited to appear here. The range of engineering fields addressed include: multi-agent systems; signal processing; pattern recognition; expert systems; nonparametric estimation; and artificial neural networks.

目次

  • 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.

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