A modified Elman neural network with a new learning rate scheme uri icon

abstract

  • 2018 Elsevier B.V. Elman neural network (ENN) is one of recurrent neural networks (RNNs). Comparing to traditional neural networks, ENN has additional inputs from the hidden layer, which forms a new layerthe context layer. So the standard back-propagation (BP) algorithm used in ENN is called Elman back-propagation algorithm (EBP). ENN can be applied to solve prediction problems of discrete time sequence. However, the EBP algorithm suffers from low convergence speed and poor generalization performance. To solve this problem, a new learning rate scheme is proposed, the convergence of new proposed scheme is proved. Furthermore, the contrast experiment is utilized to demonstrate the effectiveness of the proposed scheme from the aspects of convergence speed and consumption time with some popular schemes such as the original ENN, and PSOENN which uses PSO algorithm to search the best structure of ENN. The experience shows that the modified method proposed in this paper works best.

published proceedings

  • NEUROCOMPUTING

author list (cited authors)

  • Ren, G., Cao, Y., Wen, S., Huang, T., & Zeng, Z.

citation count

  • 102

complete list of authors

  • Ren, Guanghua||Cao, Yuting||Wen, Shiping||Huang, Tingwen||Zeng, Zhigang

publication date

  • April 2018