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Proceedings Paper

Real-time associative recognizing system for binary patterns
Author(s): Lulin Chen; Lin Lin; Ruli Wang
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Paper Abstract

This presentation describes the real-time associative recognizing system based on neural network technology for distortion-invariant pattern recognition. The system consists of two cascaded parts, i.e., an improved MADALINE invariant net with local connecting structure, and a feed-forward layered net of probabilistic logic neuron (PLN). The simple learning algorithm is proposed for the system. General-purpose RAMs are major parts of the system so that real-time processing is available.

Paper Details

Date Published: 16 September 1992
PDF: 7 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140056
Show Author Affiliations
Lulin Chen, Shanghai Institute of Technical Physics (China)
Lin Lin, Shanghai Institute of Technical Physics (China)
Ruli Wang, Shanghai Institute of Technical Physics (China)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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