Overview
The previous section described a unified computational intelligence learning architecture based on Adaptive Resonance Theory neural networks. In this chapter, this architecture is used in an application that was briefly introduced in Chapter 1.
The content of this chapter is adapted from a paper appearing in the Neural Networks journal (Brannan, Seiffertt, Draelos, & Wunsch, 2009) and a preliminary version appearing as (Brannan, Conrad, Draelos, Seiffertt, & Wunsch, 2006).
In this chapter, the unified computational intelligence algorithm is referred to as CARTMAP, for Coordinated ARTMAP. This name was determined by Sandia National Laboratories collaborators since they, being government officials, possess a certain je ne sais quoi for the use of acronyms. This application, in the area of situation awareness, proved a testing bed for a unified learning architecture of the type described in Chapter 2 of this book. The results indicate that the task described, if performed using only a single mode of learning, would not have achieved the same level of effectiveness as it did using all three modes in combination.
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Seiffertt, J., Wunsch, D.C. (2010). An Application of Unified Computational Intelligence. In: Unified Computational Intelligence for Complex Systems. Evolutionary Learning and Optimization, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03180-9_3
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DOI: https://doi.org/10.1007/978-3-642-03180-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03179-3
Online ISBN: 978-3-642-03180-9
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