Comparison of Decision Tree and Multistage Neuro-Fuzzy Classifiers of Seafloor using Wavelet Coefficients of Acoustic Echoes

摘要

In recent years the neuro-fuzzy expert systems have been successfully applied to seabed classification from acoustic echoes. In particular, implementation of multistage Incremental Fuzzy Neural Network architectures (IFNN) demonstrate good performance and high classification rate, especially when the number of input parameters was reduced by Principal Component Analysis (i.e. to the wavelet coefficients extracted from sea bottom echoes only). The paper presents the comparison of the IFNN system with the other approach, utilising most recently developed decision tree classifier, which constructs classification models by revealing and analysing patterns found in seabed echo records.