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Proceedings Paper

Parameter adaptation in a simplified pulse-coupled neural network
Author(s): Geza Szekely; Thomas Lindblad
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Paper Abstract

In a general purpose pulse coupled neural network (PCNN) algorithm the following parameters are used: 2 weight matrices, 3 time constants, 3 normalization factors and 2 further parameters. In a given application, one has to determine the near optimal parameter set to achieve the desired goal. Here a simplified PCNN is described which contains a parameter fitting part, in the least squares sense. Given input and a desired output image, the program is able to determine the optimal value of a selected PCNN parameter. This method can be extended to more general PCNN algorithms, because partial derivatives are not required for the fitting. Only the sum of squares of the differences is used.

Paper Details

Date Published: 22 March 1999
PDF: 8 pages
Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); doi: 10.1117/12.343046
Show Author Affiliations
Geza Szekely, ATOMKI Institute of Nuclear Research (Sweden)
Thomas Lindblad, Royal Institute of Technology (Sweden)


Published in SPIE Proceedings Vol. 3728:
Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks
Thomas Lindblad; Mary Lou Padgett; Jason M. Kinser, Editor(s)

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