Feed-forward Neural Networks Training : a Comparison between Genetic Algorithm and Backpropagation Learning Algorithm

@inproceedings{Che2011FeedforwardNN,
  title={Feed-forward Neural Networks Training : a Comparison between Genetic Algorithm and Backpropagation Learning Algorithm},
  author={Zhen-Guo Che and Tzu-An Chiang and Z. Che},
  year={2011}
}
This study discusses the advantages and characteristics of the genetic algorithm and back-propagation neural network to train a feed-forward neural network to cope with weighting adjustment problems. We compare the performances of a back-propagation neural network and genetic algorithm in the training outcomes of three examples by referring to the measurement indicators and experiment data. The results show that the back-propagation neural network is superior to the genetic algorithm. Also, the… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 42 references

Neural Networks

  • S. Kumar
  • McGraw-Hill, New York
  • 2005
Highly Influential
5 Excerpts

A genetic algorithm-based model for solving multi-period supplier selection problem with assembly sequence

  • Z.-H. Che
  • International Journal of Production Research, vol…
  • 2010
3 Excerpts

A multi-layer feed forward neural network model for accurate prediction of flue gas sulfuric acid dew points in process industries

  • B. ZareNezhad, A. Aminian
  • Applied Thermal Engineering, vol.30, no.6-7, pp…
  • 2010
1 Excerpt

Using hybrid genetic algorithms for multi-period product configuration change planning

  • Z.-H. Che
  • International Journal of Innovative Computing…
  • 2010
3 Excerpts

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