Configuring artificial neural networks to implement function optimization

Ramakrishnan Sundaram

doi:10.1117/12.461679

5 April 2002 Configuring artificial neural networks to implement function optimization

Ramakrishnan Sundaram

Proceedings Volume 4668, Applications of Artificial Neural Networks in Image Processing VII; (2002) https://doi.org/10.1117/12.461679
Event: Electronic Imaging, 2002, San Jose, California, United States

Abstract

Threshold binary networks of the discrete Hopfield-type lead to the efficient retrieval of the regularized least-squares (LS) solution in certain inverse problem formulations. Partitions of these networks are identified based on forms of representation of the data. The objective criterion is optimized using sequential and parallel updates on these partitions. The algorithms consist of minimizing a suboptimal objective criterion in the currently active partition. Once the local minima is attained, an inactive partition is chosen to continue the minimization. This strategy is especially effective when substantial data must be processed by resources which are constrained either in space or available bandwidth.

Citation Download Citation

Ramakrishnan Sundaram "Configuring artificial neural networks to implement function optimization", Proc. SPIE 4668, Applications of Artificial Neural Networks in Image Processing VII, (5 April 2002); https://doi.org/10.1117/12.461679

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