Share Email Print

Proceedings Paper

Pattern recognition using stochastic neural networks
Author(s): Ying Liu
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In this paper, we study pattern recognition using stochastic artificial neural networks (SANN). 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 SANN. Given an input image f (epsilon) F, one can find a SANN t (epsilon) T such that the equilibrium distribution of this SANN is the given image f. Therefore, the input image, f, is encoded into a specification of a SANN, t. This mapping from F (image space) to T (parameter space of SANN) defines SANN transformation. SANN 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 SANN. The internal change rule of our system uses a 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: 20 August 1993
PDF: 14 pages
Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, (20 August 1993); doi: 10.1117/12.150171
Show Author Affiliations
Ying Liu, Savannah State College (United States)

Published in SPIE Proceedings Vol. 2055:
Intelligent Robots and Computer Vision XII: Algorithms and Techniques
David P. Casasent, Editor(s)

© SPIE. Terms of Use
Back to Top