Abstract
In this paper, an architecture of a fuzzy probabilistic neural network is considered. A learning algorithm for the activation function parameters is proposed. The advantages of this network lie in the possibility of classification of the data with substantially overlapping clusters, and tuning of the activation function parameters improves the accuracy of classification. Simulation results confirm the efficiency of the proposed approach in the data classification problems.
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© 2003 Springer-Verlag Wien
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Bodyanskiy, Y., Gorshkov, Y., Kolodyazhniy, V., Wernstedt, J. (2003). A learning probabilistic neural network with fuzzy inference. In: Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0646-4_3
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DOI: https://doi.org/10.1007/978-3-7091-0646-4_3
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-00743-3
Online ISBN: 978-3-7091-0646-4
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