Improving the generalization performance of RBF neural networks using a linear regression technique
2009
In this paper we present a method for improving the generalization performance of a radial basis function (RBF) neural network. The method uses a statistical linear regression technique which is based on the orthogonal least squares (OLS) algorithm. We first discuss a modified way to determine the center and width of the hidden layer neurons. Then, substituting a QR algorithm for the traditional Gram-Schmidt algorithm, we find the connected weight of the hidden layer neurons. Cross-validation is utilized to determine the stop training criterion. The generalization performance of the network is further improved using a bootstrap technique. Finally, the solution method is used to solve a simulation and a real problem. The results demonstrate the improved generalization performance of our algorithm over the existing methods.
Keywords:
- QR algorithm
- Machine learning
- Artificial intelligence
- Least squares
- Mathematical optimization
- Artificial neural network
- Non-linear least squares
- Cross-validation
- Radial basis function network
- Linear regression
- Function approximation
- Mathematics
- Bootstrapping (electronics)
- Computer science
- Pattern recognition
- Radial basis function
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