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

• Autorzy: Dung T. V. , Maciołowska J. , Stepnowski A.
• Konferencja: Proceedings of the 2 nd EAA International Symposium on Hydroacoustics 24-27 May 1999, Gdańsk-Jurata POLAND
• Języki publikacji: EN

Abstrakty

EN

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.

• Wydawca: Polskie Towarzystwo Akustyczne. Oddział Gdański
• Czasopismo: Hydroacoustics
• Rocznik: 1999
• Tom: Vol. 2
• Strony: 79--84
• Opis fizyczny: Bibliogr. 7 poz., rys., tab.

Twórcy

autor

Dung T. V.

• Technical University of Gdańsk, Acoustics Department, 80-952 Gdańsk, Poland

autor

Maciołowska J.

• Technical University of Gdańsk, Acoustics Department, 80-952 Gdańsk, Poland

autor

Stepnowski A.

• Technical University of Gdańsk, Acoustics Department, 80-952 Gdańsk, Poland

Bibliografia

• 1. Anon. MATLAB - Fuzzy Logic Toolbox User's Guide, Ver.2, 1998.
• 2. J.S. Duan, F.L. Chung, Madami Type Multistage Fuzzy Neural-Network Model, Proceedings of the IEEE Word Congress on Computational Intelligence, Anchorage, Alaska, Fuzzy-IEEE pp. 1253-1258, (1998).
• 3. J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing - A Computational Approach to Learning and Machine Intelligence, Prentice-Hall International, Inc., 1997.
• 4. R. Komendarczyk, Comparison of selected classifiers in a sea-bottom recognition task, Proceedings of the International Symposium on Hydroacoustics and Ultrasonics, Gdansk-Jurata, pp. 125-134, (1997).
• 5. J. Maciołowska, A. Stepnowski, T.V. Dung, Fish Schools and Seabed Identification Using Neural Networks and Fuzzy Logic Classifiers, Proceedings of the Fourth European Conference on Underwater Acoustics, Rome, pp. 275-280, (1998).
• 6. J. Maciolowska, A. Stepnowski, Fuzzy Expert System for Pelagic Fish Schools Identification, Proceedings of XLIV Open Seminary on Acoustics, Jastrzebia Gora, pp. 447-452, (1997).
• 7. A. Stepnowski, T. V. Dung, J. Maciołowska, Analysis of the Neuro-Fuzzy Classifiers for Fish Species Identification and Bottom Typing from Acoustic Echoes, Proceedings of the Forum Acusticum 1999, TU Berlin, Berlin, (1999).