A new random search method for neural network learning-RasID

This paper presents a novel random searching scheme called RasID for neural networks training. The idea is to introduce a sophisticated probability density function (PDF) for generating search vector. The PDF provides two parameters for realizing intensified search in the area where it is likely to...

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Bibliographic Details
Published in1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227) Vol. 3; pp. 2346 - 2351 vol.3
Main Authors Jinglu Hu, Hirasawa, K., Mutata, J., Ohbayashi, M., Eki, Y.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1998
Subjects
Computer simulation
Genetic algorithms
Information science
Modeling
Multidimensional systems
Neural networks
Optimization methods
Pattern recognition
Probability density function
Search methods
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Summary:This paper presents a novel random searching scheme called RasID for neural networks training. The idea is to introduce a sophisticated probability density function (PDF) for generating search vector. The PDF provides two parameters for realizing intensified search in the area where it is likely to find good solutions locally or diversified search in order to escape from a local minimum based on the success-failure of the past search. Gradient information is used to improve the search performance. The proposed scheme is applied to layered neural networks training and is benchmarked against other deterministic and nondeterministic methods.
ISBN:0780348591
9780780348592
ISSN:1098-7576
1558-3902
DOI:10.1109/IJCNN.1998.687228