An individual adaptive gain parameter backpropagation algorithm for complex-valued neural networks

  • Authors:

  • Songsong Li;Toshimi Okada;Xiaoming Chen;Zheng Tang

  • Affiliations:

  • Faculty of Engineering, Toyama Prefectural University, Toyama, Japan;Faculty of Engineering, Toyama Prefectural University, Toyama, Japan;Faculty of Engineering, Toyama University, Toyama, Japan;Faculty of Engineering, Toyama University, Toyama, Japan

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
  • Year:
  • 2006

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Abstract

The complex-valued backpropagation algorithm has been widely used. However, the local minima problem usually occurs in the process of learning. We proposed an individual adaptive gain parameter backpropagation algorithm for complex-valued neural network to solve this problem. We specified the gain parameter of the sigmoid function in the hidden layer for each learning pattern. The proposed algorithm is tested by benchmark problem. The simulation results show that it is capable of preventing the complex-valued network learning from sticking into the local minima.