Abstract
This paper evaluates the performance of ten significance measures applied to the problem of determining an appropriate network structure, for data modelling with neurofuzzy systems. The advantages of Neurofuzzy systems are demonstrated with application to both real and synthetic data interpretation problems.
Keywords
- Singular Value Decomposition
- Model Size
- Training Pattern
- Multivariate Adaptive Regression Spline
- Minimum Descriptor Length
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© 1997 Springer-Verlag
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Gunn, S.R., Brown, M., Bossley, K.M. (1997). Network performance assessment for Neurofuzzy data modelling. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052850
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DOI: https://doi.org/10.1007/BFb0052850
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63346-4
Online ISBN: 978-3-540-69520-2
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