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Proceedings Paper

Using a generalized neuron evolution rule to resolve spatial deformation in pattern recognition
Author(s): Andrew C. C. Cheng; Ling Guan
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Paper Abstract

In common associative neural network practice, the updating rule remains unchanged during neuron evolution. Once the evolution is stuck in a local minimum, simulated annealing is used to resolve the problem. In this paper, the local minimum problem in associative neural networks is treated from a novel perspective. In particular a generalized neuron evolution algorithm is introduced. It is shown in the paper that the probability of local minima associated with different learning schemes having the same locations on the surfaces of their respective energy functions is extremely small. Thus once evolution terminates in a local minimum associated with one learning scheme, another learning scheme is invoked to continue the evolution. Numerical examples are used to demonstrate the performance of this. The results show that the local minimum problem is effectively resolved.

Paper Details

Date Published: 16 September 1994
PDF: 10 pages
Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); doi: 10.1117/12.186000
Show Author Affiliations
Andrew C. C. Cheng, Univ. of Sydney (Australia)
Ling Guan, Univ. of Sydney (Australia)


Published in SPIE Proceedings Vol. 2308:
Visual Communications and Image Processing '94
Aggelos K. Katsaggelos, Editor(s)

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