Abstract
In this paper, a novel shape recognition method based on radial basis probabilistic neural network (RBPNN) is proposed. The orthogonal least square algorithm (OLSA) is used to train the RBPNN and the recursive OLSA is adopted to optimize the structure of the RBPNN. A leaf image database is used to test the proposed method. And a modified Fourier method is applied to descript the shape of the plant leaf. The experimental result shows that the RBPNN achieves higher recognition rate and better classification efficiency with respect to radial basis function neural network (RBFNN), BP neural network (BPNN) and multi-Layer perceptron network (MLPN) for the plant species identification.
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© 2005 Springer-Verlag Berlin Heidelberg
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Du, J., Huang, D., Wang, X., Gu, X. (2005). Shape Recognition Based on Radial Basis Probabilistic Neural Network and Application to Plant Species Identification. 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_45
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DOI: https://doi.org/10.1007/11427445_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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