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Adaptive neuro-fuzzy and fuzzy decision tree classifiers as applied to seafloor characterization

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Abstract

This paper investigates the influence of various backscattered echo parameters collected at an echosounder frequency of 200 kHz on the performance of the neuro-fuzzy and fuzzy decision tree classifiers of a seabed. In particular, the wavelet coefficients of the bottom echo were investigated along with other echo parameters like energy, amplitude, slope of the falling part of the echo, etc. The data were processed in an Adaptive Neuro Fuzzy Inference System (ANFIS), which was implemented in two multistage structures, viz.; Incremental Fuzzy Neural Network (IFNN) and Aggregated Fuzzy Neural Network (AFNN). The number of input parameters for the networks was reduced by using the Principal Component Analysis (PCA). A fuzzy decision tree algorithm was developed and used directly (without PCA data reduction) in the classification procedure utilizing the same data. The performances of both approaches were analyzed and compared.

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Authors and Affiliations

  1. Department of Remote Sensing Systems, Gdańsk University of Technology, ul. Narutowicza 11/12, Gdańsk, 80-952, Poland

    A. Stepnowski, M. Moszyński & Tran Van Dung

Authors
  1. A. Stepnowski
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  2. M. Moszyński
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  3. Tran Van Dung
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Additional information

This paper is dedicated to the memory of the outstanding Russian acoustician and my dear friend and colleague Professor Leonid M. Lyamshev.

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Translated from Akusticheski\(\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\smile}$}}{l} \) Zhurnal, Vol. 49, No. 2, 2003, pp. 233–244.

Original English Text Copyright © 2003 by Stepnowski, Moszyn ski, Tran Van Dung.

This paper was submitted by the author in English.

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Stepnowski, A., Moszyński, M. & Van Dung, T. Adaptive neuro-fuzzy and fuzzy decision tree classifiers as applied to seafloor characterization. Acoust. Phys. 49, 193–202 (2003). https://doi.org/10.1134/1.1560382

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  • DOI: https://doi.org/10.1134/1.1560382

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