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Structure Perturbation Optimization for Hopfield-Type Neural Networks
- Source :
- Artificial Neural Networks and Machine Learning – ICANN 2014 ISBN: 9783319111780, ICANN
- Publication Year :
- 2014
- Publisher :
- Springer International Publishing, 2014.
-
Abstract
- In this paper, we extract the core idea of state perturbation from Hopfield-type neural networks and define state perturbation formulas to describe the general way of optimization methods. Departing from the core idea and the formulas, we propose a novel optimization method related to neural network structure, named structure perturbation optimization. Our method can produce a structure transforming process to retrain Hopfield-type neural networks to get better problem-solving ability. Experiments validate that our method effectively helps Hopfield-type neural networks to escape from local minima and get superior solutions.
Details
- ISBN :
- 978-3-319-11178-0
- ISBNs :
- 9783319111780
- Database :
- OpenAIRE
- Journal :
- Artificial Neural Networks and Machine Learning – ICANN 2014 ISBN: 9783319111780, ICANN
- Accession number :
- edsair.doi...........0c3101bba73d3a75a034850160afd7e6