KOHONEN NETWORKS AS SHIPS CLASSIFIER ANDRZEJ ŻAK

The paper presents the technique of artificial neural networks used as classifier of hydroacoustic signatures generated by moving ship. In the paper firstly the method of feature extraction from hydroacoustic signatures using calculation of Mel-Frequency Cepstral Coefficients was discussed. Next the method of feature matching using for purpose of object classification basing on hydroacoustic signatures was described. The technique of artificial neural networks especially Kohonen networks which belongs to group of self organizing networks where chosen to solve the research problem of classification. The choice was caused by some advantages of mentioned kind of neural networks for example they are ideal for finding relationships amongst complex sets of data, they have possibility to self expand the set of answers for new input vectors. To check the correctness of classifier work the research in which the number of right classification for presented and not presented before hydroacoustic signatures were made. Some results of research were presented on this paper