Abstract
This paper deals with some theoretic and numerical issues in the learning algorithm for Procedure Neural Networks (PNNs). In PNNs the weights are time functions and can be expended by some basis functions. The properties of PNNs vary with the choice of weights functions. Orthodoxy basis functions have many advances in expending the weight functions and save training time in PNNs learning. In this paper several kinds of orthodoxy functions are proposed. Also the algorithm convergence of PNNs training is discussed.
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References
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© 2004 Springer-Verlag Berlin Heidelberg
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Jia, J., Liang, Jz. (2004). Orthodoxy Basis Functions and Convergence Property in Procedure Neural Networks. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_26
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DOI: https://doi.org/10.1007/978-3-540-24844-6_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22123-4
Online ISBN: 978-3-540-24844-6
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