Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
A new neural network for cluster-detection-and-labeling
IEEE Transactions on Neural Networks
Selecting radial basis function network centers with recursive orthogonal least squares training
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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This paper proposes an improvement of the Cluster Detection and Labeling Neural Network. The original classifier criterion has been modified by introducing Elliptical Basis Functions (EBF) as transfer function of the hidden neurons. In the original CDL network, a similarity criterion is used to determine the membership to prototypes and then to classes. By introducing EBF, we have introduced degrees of membership leading to elliptic shape of classes. In this paper, the functioning of the original CDL network is summarized. Then, the improvements of the architecture in terms of network architecture, neuron activation function and learning stages are described. We present the improvement with EBF and the modification of the auto-adaptation neural network abilities. As validations of our architecture, we illustrate its benefits in comparison with the original CDL network.