Abstract
This paper compares the application of different neural models-multilayer perceptrons, radial basis functions and B-splines - for a benchmark problem, and illustrates the applicability of a common learning algorithm for all models considered. The learning algorithm is employed both for off-line training and for on-line model adaptation. In the latter case, a sliding window of past learning data is employed.
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© 2003 Springer-Verlag Berlin Heidelberg
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Ruano, A.E.B. (2003). Comparison of Neural Models, Off-line and On-line Learning Algorithms for a Benchmark Problem. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_58
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DOI: https://doi.org/10.1007/3-540-44869-1_58
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