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Proceedings Paper

Pattern recognition using stochastic cellular automata
Author(s): Ying Liu
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Paper Abstract

In this paper, we study pattern recognition using stochastic cellular automata (SCA). A learning system can be defined by three rules: the encoding rule, the rule of internal change, and the quantization rule. In our system, the data encoding is to store an image in a stable distribution of a SCA. Given an input image f (epsilon) F, one can find a SCA t (epsilon) T such that the equilibrium distribution of this SCA is the given image f. Therefore, the input image, f, is encoded into a specification of a SCA, t. This mapping from F (image space) to T (parameter space of SCA) defines SCA transformation. SCA transformation encodes an input image into a relatively small vector which catches the characteristics of the input vector. The internal space T is the parameter space of SCA. The internal change rule of our system uses local minima algorithm to encode the input data. The output data of the encoding stage is a specification of a stochastic dynamical system. The quantization rule divides the internal data space T by sample data.

Paper Details

Date Published: 1 September 1993
PDF: 12 pages
Proc. SPIE 1962, Adaptive and Learning Systems II, (1 September 1993); doi: 10.1117/12.150594
Show Author Affiliations
Ying Liu, Savannah State College (United States)


Published in SPIE Proceedings Vol. 1962:
Adaptive and Learning Systems II
Firooz A. Sadjadi, Editor(s)

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