Level Crossing Prediction with Neural Networks View Full Text


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Article Info

DATE

2009-08-13

AUTHORS

Halfdan Grage, Jan Holst, Georg Lindgren, Mietek Saklak

ABSTRACT

A level crossing predictor or alarm system with prediction horizon k is said to be optimal if it, at time t detects that an upcrossing will occur at time t + k, with a certain high probability and simultaneously gives a minimum number of false alarms. For a Gaussian stationary process, the optimal level crossing predictor can be explicitly specified in terms of the predicted value of the process itself and of its derivative. To the authors knowledge this simple optimal solution has not been used to any substantial degree. In this paper it is shown how a neural network can be trained to approximate an optimal alarm system arbitrarily well. As in other methods of parametrization, the choice of model structure, as well as an appropriate representation of data, are crucial for a good result. Comparative studies are presented for two Gaussian ARMA-processes, for which the optimal predictor can be derived theoretically. These studies confirm that a properly trained neural network can indeed approximate an optimal alarm system quite well – with due attention paid to the problems of model structure and representation of data. The technique is also tested on a strongly non-Gaussian Duffing process with satisfactory results. More... »

PAGES

623-645

References to SciGraph publications

  • 2005-04-08. An improved back propagation algorithm topredict episodes of poor air quality in SOFT COMPUTING
  • 1993. Statistical aspects of neural networks in NETWORKS AND CHAOS — STATISTICAL AND PROBABILISTIC ASPECTS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11009-009-9153-3

    DOI

    http://dx.doi.org/10.1007/s11009-009-9153-3

    DIMENSIONS

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