Abstract: The dynamic decay adjustment (DDA) algorithm is a fast constructive algorithm for training RBF neural networks. In previous works it has been ...
The results show that the proposed method considerably improves the performance of RBF-DDA with default parameters on these tasks. The results are compared to ...
In this paper we present a method for optimizing RBF-. DDA performance by selection of appropriate θ− value. In our method, RBF-DDA training is carried out in ...
The proposed method for selecting the value of /spl theta//sup -/ for performance optimization considerably improves the performance of RBF-DDA with default ...
This paper proposes a method for improving RBF-DDA generalization performance by adjusting the weights of the connections between hidden and output units. The ...
The Dynamic Decay Adjustment (DDA) algorithm is a fast constructive algorithm for training RBF neural networks. In previous works it has been shown that for ...
Improving RBF-DDA Performance on Optical Character Recognition through Parameter Selection ICPR, 2004. ICPR v4 2004 · DBLP · Scholar · DOI. Full names. Links
This paper proposes a method for improving RBF-DDA generalization performance by combining a data reduction technique with the parameter selection technique.
Improving constructive training of RBF networks through selective ...
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Improving RBF-DDA Performance on Optical Character Recognition through Weights Adjustment · Computer Science. The 2006 IEEE International Joint Conference on…
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The dynamic decay adjustment (DDA) algorithm is a fast constructive algorithm for training RBF neural networks (RBFNs) and probabilistic neural networks ...