Using a generalized neuron evolution rule to resolve spatial deformation in pattern recognition

Andrew C. C. Cheng; Ling Guan

doi:10.1117/12.186000

16 September 1994 Using a generalized neuron evolution rule to resolve spatial deformation in pattern recognition

Andrew C. C. Cheng, Ling Guan

Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994) https://doi.org/10.1117/12.186000
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States

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.

Citation Download Citation

Andrew C. C. Cheng and Ling Guan "Using a generalized neuron evolution rule to resolve spatial deformation in pattern recognition", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); https://doi.org/10.1117/12.186000

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