Self-organizing network for optimum supervised learning

  title={Self-organizing network for optimum supervised learning},
  author={Manoel Fernando Tenorio and Wei-Tsih Lee},
  journal={IEEE transactions on neural networks},
  volume={1 1},
A new algorithm called the self-organizing neural network (SONN) is introduced. Its use is demonstrated in a system identification task. The algorithm constructs a network, chooses the node functions, and adjusts the weights. It is compared to the backpropagation algorithm in the identification of the chaotic time series. The results show that SONN constructs a simpler, more accurate model, requiring less training data and fewer epochs. The algorithm can also be applied as a classifier. 
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