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Twinned Topographic Maps for Decision Making in the Cockpit

  • Steve Thatcher
  • Colin Fyfe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)

Abstract

There is consensus amongst aviation researchers and practitioner that some 70% of all aircraft accidents have human error as a root cause [1]. Thatcher, Fyfe and Jain [2] have suggested an intelligent landing support system, comprising of three agents, that will support the flight crew in the most critical phase of a flight, the approach and landing. The third agent is envisaged to act as a pattern matching agent or an ‘extra pilot’ in the cockpit to aid decision making. This paper will review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [3]. But whereas the GTM is an extension of a mixture of experts, our new model is an extension of a product of experts [4]. We show visualisation results on some real and artificial data sets and compare with the GTM. We then introduce a second mapping based on harmonic averages and show that it too creates a topographic mapping of the data.

Keywords

Latent Point Topographic Mapping Data Space Latent Variable Model Harmonic Average 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Steve Thatcher
    • 1
  • Colin Fyfe
    • 2
  1. 1.Aviation Education, Research and Operations Laboratory (AERO Lab)University of South Australia
  2. 2.Applied Computational Intelligence Research UnitThe University of PaisleyScotland

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