Sea boftom typing using neuro-fuzzy classifier operating on multi-frequency data

A hybrid neuro-fuzzy classifier was development for sea-boftom identification from acoustic echoes. A multistage ANFIS structure was constructed and tested on data collected on 38kHz and 120kHz echosounder's frequencies. In multistage systems available data is processed in stages. The decisions about assigning a boftom echo, represented by digitised echo envelope's parameters. to one of the classes is made hierarchically. Firstly, an approximate decision is made based only on one set of input variables. The decision is then fine-tuned by considering more and more factors, it is in following stages next parameters are taken under account until the final decision, corresponding to the output class. is made. The proposed approach nof only gives better classification results, as compared to paralleI ANFIS system, but also it demands less computation power.