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A novel optimized GA–Elman neural network algorithm

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Abstract

The Elman neural network has good dynamic properties and strong global stability, being most widely used to deal with nonlinear, dynamic, and complex data. However, as an optimization of the backpropagation (BP) neural network, the Elman model inevitably inherits some of its inherent deficiencies, influencing the recognition precision and operating efficiency. Many improvements have been proposed to resolve these problems, but it has proved difficult to balance the many relevant features such as storage space, algorithm efficiency, recognition precision, etc. Also, it is difficult to obtain a permanent solution from a temporary solution simultaneously. To address this, a genetic algorithm (GA) can be introduced into the Elman algorithm to optimize the connection weights and thresholds, which can prevent the neural network from becoming trapped in local minima and improve the training speed and success rate. The structure of the hidden layer can also be optimized using the GA, which can solve the difficult problem of determining the number of neurons. Most previous studies on such evolutionary Elman algorithms optimized the connection weights or network structure individually, which represents a slight deficiency. We propose herein a novel optimized GA–Elman neural network algorithm where the connection weights are real-encoded, while the neurons of the hidden layer also adopt real-coding but with the addition of binary control genes. In this new algorithm, the connection weights and the number of hidden neurons are optimized using hybrid encoding and evolution simultaneously, greatly improving the performance of the resulting novel GA–Elman algorithm. The results of three experiments show that this new GA–Elman model is superior to the traditional model in terms of all calculated indexes.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (nos. 61572300, 31571571, and 61379101), Natural Science Foundation of Shandong Province in China (no. ZR2014FM001) and Taishan Scholar Program of Shandong Province of China (no. TSHW201502038).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China

    Weikuan Jia, Yuanjie Zheng & Sujuan Hou

  2. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China

    Dean Zhao

  3. School of Agricultural Engineering, Shandong University of Technology, Zibo, 255000, China

    Weikuan Jia

  4. Key Lab of Intelligent Computing & Information Security in Universities of Shandong, Institute of Life Sciences, Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, and Key Lab of Intelligent Information Processing, at Shandong Normal University, Jinan, 250014, China

    Yuanjie Zheng

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  1. Weikuan Jia
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  2. Dean Zhao
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  3. Yuanjie Zheng
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  4. Sujuan Hou
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Correspondence to Weikuan Jia.

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Jia, W., Zhao, D., Zheng, Y. et al. A novel optimized GA–Elman neural network algorithm. Neural Comput & Applic 31, 449–459 (2019). https://doi.org/10.1007/s00521-017-3076-7

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