Evolution of Real Valued Weights for RBF-DDA Networks

The radial basis function network with dynamic decay adjustment is a fast adaptive classifier. Its specific property is the utilization of integers as weights, counting those training samples that are elements of a certain radial region in the data space. In our experimentation we allow real valued weights instead of integers during their evolution. With real values the application of an evolutionary algorithm increases classification performance. An additional study shows the effects of pruning neurons with weights, that evolved to values lower than one. Overall, the model with real valued weights performs better or its network topology becomes less complex.