Data fusion using dynamic associative memory

Titus K. Lo, Henry Leung, Chun Chung Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSociety of Photo-Optical Instrumentation Engineers
Number of pages12
ISBN (Print)0819424838
Publication statusPublished - 1 Dec 1997
Externally publishedYes
EventSignal Processing, Sensor Fusion, and Target Recognition VI - Orlando, FL, United States
Duration: 21 Apr 199724 Apr 1997


ConferenceSignal Processing, Sensor Fusion, and Target Recognition VI
Country/TerritoryUnited States
CityOrlando, FL

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics


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