Abstract
This paper is concerned with an automatic classification of interphones into good and bad clusters based on the spectral data using two kinds of neural networks. One is the self-organizing map (SOM) by Kohonen and the other is the layered neural network (LNN) using the error back-propagation method. The SOM is used to find the representative teaching data for each cluster in order to achieve the fast convergence of learning of the LNN and reduction of the network size. The LNN is used to classify the input data into good and bad clusters. From the real data classification of interphones, we can see that the proposed method using two kinds of NNs could classify the data more precisely compared with the case using only a conventional LNN.
