Abstract
This paper presents a new divide-and-conquer learning approach to radial basis function networks (DCRBF). The DCRBF network is a hybrid system consisting of several sub-RBF networks, each of which takes a sub-input space as its input. Since this system divides a high-dimensional modeling problem into several low-dimensional ones, it can considerably reduce the structural complexity of a RBF network, whereby the net’s learning becomes much faster. We have empirically shown its outstanding learning performance on forecasting two real time series as well as synthetic data in comparison with a conventional RBF one.
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Huang, Rb., Cheung, Ym. (2003). A Fast Implementation of Radial Basis Function Networks with Application to Time Series Forecasting. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_21
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DOI: https://doi.org/10.1007/978-3-540-45080-1_21
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