Abstract
The capabilities and robustness of a new spiking neural network (SNN) learning algorithm are demonstrated with sound classification and function approximation applications. The proposed SNN learning algorithm and the radial basis function (RBF) learning method for function approximation are compared. The complexity of the learning algorithm is analyzed.
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Amin, H.H., Fujii, R.H. (2005). Sound Classification and Function Approximation Using Spiking Neural Networks. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_65
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DOI: https://doi.org/10.1007/11538059_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28226-6
Online ISBN: 978-3-540-31902-3
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