Referenced Items are not available for this document.
A computationally efficient methodology for overcoming local minima in nonlinear neural network learning is presented. This methodology is based on the newly discovered TRUST global optimization paradigm. Enhancements to the backpropagation schema in feedforward multilayer architectures, and to adjoint-operator learning in recurrent networks are discussed. Extensions to TRUST now formally guarantee reaching a global minimum in the multidimensional case. Results for a standard benchmark are included, to illustrate the theoretical developments
Published in:
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
(Volume:7
)
Date of Conference: 27 Jun-2 Jul 1994
- Page(s):
- 4592 - 4596 vol.7
- Meeting Date :
- 27 Jun 1994-02 Jul 1994
- Print ISBN:
- 0-7803-1901-X
- INSPEC Accession Number:
- 4962672
- Conference Location :
- Orlando, FL
- Digital Object Identifier :
- 10.1109/ICNN.1994.375015
- Product Type:
- Conference Publications
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