Abstract
Detection fertility and development in hatchery eggs could increase efficiency in commercial hatcheries. A new algorithm named simulated annealing particle swarm optimization algorithm (SAPSO) is proposed, and it is used to optimize topology structure of multi-layer feedback forward neural network for classification of hatching eggs. Trained and tested by a great deal of samples, a reasonable neural network model is obtained. Its performance is measured in terms of two parameters: short computing time and accuracy in the classification process.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhi-hong, Y., Chun-guang, W., Jun-qing, F. (2006). SAPSO Neural Network for Inspection of Non-development Hatching Eggs. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_13
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DOI: https://doi.org/10.1007/11881070_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
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