Abstract
In general, wavelet neural networks have a problem on the curse of dimensionality, i.e. the number of hidden units to be required are exponentially rose with increasing an input dimension. To solve the above problem, a wavelet neural network incorporating a local linear model has already been proposed. On their network design, however, the number of hidden units is empirically determined and fixed during learning. In the present paper, a design method based on Bayesian method is proposed for the local linear wavelet neural network. The performance of the proposed method is evaluated through computer simulation.
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Kobayashi, K., Obayashi, M., Kuremoto, T. (2009). A Bayesian Local Linear Wavelet Neural Network. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_18
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DOI: https://doi.org/10.1007/978-3-642-03040-6_18
Publisher Name: Springer, Berlin, Heidelberg
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