Multiple neural networks-integrated underwater target classification based on fuzzy theory

Abstract

In the area of underwater target recognition, in order to process various target feature data extracted from underwater radiating noise signals, a target classification method based on the integration of multiple fuzzy clustering neural networks and fuzzy comprehensive analysis is put forward in this paper. An adaptive fuzzy clustering neural network classifier is constructed and a relative studying algorithm is described for the first class classification, which is applied separately to different kinds of feature data from LOFAR spectrum, bispectrum, and wavelet transformation. Based on fuzzy comprehensive analysis of the outputs and probabilities of the above neural network classifiers and the category and distribution of test samples, the second class fuzzy classifying rules are designed to preprocess and assess the categories of ships. More than 4000 passive sonar signal samples are collected and many tests are done, and the results show the feasibility, adaptability, and effectiveness of the present method.