Abstract
The fusion system designing of multiple classifiers, which is based on the radial basis probabilistic neural network (RBPNN), is discussed in this paper. By means of the proposed design method, the complex structure optimization can be effectively avoided in the designing procedure of the RBPNN. In addition, D-S fusion algorithm adopted in the system greatly improves the classification performance for the complexity problem of the real-world. The simulation results demonstrate that the designing case of the fusion system based on the RBPNNs is feasible and effective.
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Zhao, WB., Zhang, MY., Wang, LM., Du, JY., Huang, DS. (2004). Multiple Classifiers Fusion System Based on the Radial Basis Probabilistic Neural Networks. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_46
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DOI: https://doi.org/10.1007/978-3-540-28651-6_46
Publisher Name: Springer, Berlin, Heidelberg
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