Data fusion using dynamic associative memory

Titus K. Y. Lo; Henry Leung; Keith C. C. Chan

doi:10.1117/12.280815

28 July 1997 Data fusion using dynamic associative memory

Titus K. Y. Lo, Henry Leung, Keith C. C. Chan

Proceedings Volume 3068, Signal Processing, Sensor Fusion, and Target Recognition VI; (1997) https://doi.org/10.1117/12.280815
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

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Titus K. Y. Lo, Henry Leung, and Keith C. C. Chan "Data fusion using dynamic associative memory", Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); https://doi.org/10.1117/12.280815

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