A SOFT-backpropagation algorithm for training neural networks
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Date
2002-03
Journal Title
Journal ISSN
Volume Title
Type
Article
Publisher
IEEE
Series Info
Proceedings of the Nineteenth National Radio Science Conference;7522573
Scientific Journal Rankings
Abstract
The backpropagation (BP) algorithm is a one of the most common algorithms used in the training of neural networks. The single offspring technique (SOFT algorithm) is a new technique (see Likartsis, A. et al., Proc. 9th Int. Conf. on Tools with Artificial Intelligence, p.32-6, 1997; Yao, X., Proc. IEEE, vol.87, p.1425-47, 1999) of applying the genetic algorithm in the training of neural networks which reduces the training time as compared with the backpropagation algorithm. We introduce a new technique. This technique is a hybrid SOFT-BP algorithm where the SOFT-algorithm is applied first to obtain an initially good weight vector. This vector is introduced to the backpropagation algorithm, which improves the precession of the weight vector to reach an acceptable error limit. The results show an acceptable improvement in the training speed for the hybrid technique as compared with the individual backpropagation or SOFT algorithm. We also study the success ratio (how many times the algorithm succeeds in finding a solution to the total number of trials) for the new hybrid algorithm. A recommended range of the switching error limit at which to switch from the SOFT algorithm to the BP algorithm is suggested.
Description
Keywords
University for Modern Sciences and Arts, MSA University, October University for Modern Sciences and Arts, جامعة أكتوبر للعلوم الحديثة والآداب, Neural networks , Biological neural networks , Genetic algorithms , Switches , On the job training , Neurons INSPEC: Controlled Indexing backpropagation , neural nets , genetic algorithms INSPEC: Non-Controlled Indexing backpropagation , neural network training , single offspring technique , genetic algorithm , weight vector , success ratio
Citation
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