Abstract
FuNe I is a Classificator based on a Fuzzy-Neural Architecture. Physically interpretable fuzzy linguistic rules are generated from numerical sample data using supervised learning in the first phase and the antecedent membership functions are tuned in the second phase. The posteriori reduction of input features and the possibility of integrating partial apriori knowledge into the trained network are the special features of FuNe I.
Identification of underwater images in realistic situations is a tedious task. This paper describes the application of FuNe I for identification of underwater sonar images. The images are preprocessed into numerical data sets before classification. Authors also present results of conventional classifiers and a multilayer perceptron for comparison.
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© 1993 Springer-Verlag London Limited
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Halgamuge, S.K., Poechmueller, W., Ting, S., Hoehn, M., Glesner, M. (1993). Identification of Underwater Sonar Images Using Fuzzy-Neural Architecture FuNe I. In: Gielen, S., Kappen, B. (eds) ICANN ’93. ICANN 1993. Springer, London. https://doi.org/10.1007/978-1-4471-2063-6_267
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DOI: https://doi.org/10.1007/978-1-4471-2063-6_267
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