Abstract
There is a function of dynamic mapping when processing non-linear complex data with Elman neural networks. Because Elman neural network inherits the feature of back-propagation neural network to some extent, it has many defects; for example, it is easy to fall into local minimum, the fixed learning rate, the uncertain number of hidden layer neuron and so on. It affects the processing accuracy. So we optimize the weights, thresholds and numbers of hidden layer neurons of Elman networks by genetic algorithm. It improves training speed and generalization ability of Elman neural networks to get the optimal algorithm model. It has been proved by instance analysis that new algorithm was superior to the traditional model in terms of convergence rate, predicted value error, number of trainings conducted successfully, etc. It indicates the effect of the new algorithm and deserves further popularization.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Nos. 41074003, 60975039) and the Opening Foundation of Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (No. IIP2010-1).
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Ding, S., Zhang, Y., Chen, J. et al. Research on using genetic algorithms to optimize Elman neural networks. Neural Comput & Applic 23, 293–297 (2013). https://doi.org/10.1007/s00521-012-0896-3
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DOI: https://doi.org/10.1007/s00521-012-0896-3