Abstract
The selection of prototype pattern vectors and the reconstruction of order parameters are choke points in Synergetic Neural Network (SNN). We have improved the performance of SNN on these two points by Immunity Clonal Strategy (ICS) and applied them to classification successfully. But how to aggregate them remains an open question. Inspired by decision fusion mechanism, a new Immunity Clonal Synergetic Network is proposed to solve the two problems simultaneously. With the use of fuzzy integral, not only are the classification results combined but that the relative importance of the different networks is also considered. Experiments show that the presented algorithm has higher recognition rate and is more robust to classification.
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© 2005 Springer-Verlag Berlin Heidelberg
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Ma, X., Wang, S., Jiao, L. (2005). Robust Classification of Immunity Clonal Synergetic Network Inspired by Fuzzy Integral. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_5
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DOI: https://doi.org/10.1007/11427445_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
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