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.
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Thatcher, S., Fyfe, C. (2006). Twinned Topographic Maps for Decision Making in the Cockpit. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004_66
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DOI: https://doi.org/10.1007/11893004_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46537-9
Online ISBN: 978-3-540-46539-3
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