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Proceedings Paper

Data fusion using dynamic associative memory
Author(s): Titus K. Y. Lo; Henry Leung; Keith C. C. Chan
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Paper Abstract

An associative memory, unlike an addressed memory used in conventional computers, is content addressable. That is, storing and retrieving information are not based on the location of the memory cell but on the content of the information. There are a number of approaches to implement an associative memory, one of which is to use a neural dynamical system where objects being memorized or recognized correspond to its basic attractors. The work presented in this paper is the investigation of applying a particular type of neural dynamical associative memory, namely the projection network, to pattern recognition and data fusion. Three types of attractors, which are fixed-point, limit- cycle, and chaotic, have been studied, evaluated and compared.

Paper Details

Date Published: 28 July 1997
PDF: 12 pages
Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); doi: 10.1117/12.280815
Show Author Affiliations
Titus K. Y. Lo, McMaster Univ. (Canada)
Henry Leung, Defence Research Establishment Ottawa (Canada)
Keith C. C. Chan, Hong Kong Polytechnic Univ. (Hong Kong)


Published in SPIE Proceedings Vol. 3068:
Signal Processing, Sensor Fusion, and Target Recognition VI
Ivan Kadar, Editor(s)

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