Abstract:
It is known that centers, widths, and weights are three mainly considered factors in constructing a radial basis function (RBF) network. This paper aims at constructing a...Show MoreMetadata
Abstract:
It is known that centers, widths, and weights are three mainly considered factors in constructing a radial basis function (RBF) network. This paper aims at constructing a compact RBF network with two main steps. In the first step, the coarse clusters computed from triangle inequalities are refined to obtain the locations of centers by the defined maximum degree spanning tree (MDST). Meanwhile the coarse widths are obtained. In the second step, a learning algorithm referred to as anisotropic gradient descent method is presented to further refine the above coarse widths. Experiments of the proposed algorithm show its great performance in times series prediction and classification.
Published in: Journal of Systems Engineering and Electronics ( Volume: 20, Issue: 6, December 2009)
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- IEEE Keywords
- Index Terms
- Radial Basis Function ,
- Compact Network ,
- Radial Basis Function Network ,
- Time Series ,
- Gradient Descent ,
- Radial Function ,
- Central Location ,
- Gradient Descent Method ,
- Triangle Inequality ,
- Function Approximation ,
- Hidden Neurons ,
- Time Series Prediction ,
- Clustering Strategy ,
- Reproducing Kernel Hilbert Space ,
- Gray Points ,
- Small Edge
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Radial Basis Function ,
- Compact Network ,
- Radial Basis Function Network ,
- Time Series ,
- Gradient Descent ,
- Radial Function ,
- Central Location ,
- Gradient Descent Method ,
- Triangle Inequality ,
- Function Approximation ,
- Hidden Neurons ,
- Time Series Prediction ,
- Clustering Strategy ,
- Reproducing Kernel Hilbert Space ,
- Gray Points ,
- Small Edge
- Author Keywords