Abstract
In this paper, the processing of sonar signals has been carried out using Minimal Resource Allocation Network (MRAN), Probabilistic Neural Network (PNN) and Fuzzy Artmap (FAM) in differentiation of commonly encountered features in indoor environments. The stability-plasticity behaviors of all three networks have been investigated. The experimental result shows that MRAN possesses lower network complexity but experiences higher plasticity in comparison to PNN and FAM. The study also shows that MRAN performance is superior in terms of on-line learning than PNN and FAM.
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© 2004 Springer-Verlag Berlin Heidelberg
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Loo, C.K., Lim, W.S., Rao, M.V.C. (2004). Classifiers for Sonar Target Differentiation. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_39
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DOI: https://doi.org/10.1007/978-3-540-30133-2_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23206-3
Online ISBN: 978-3-540-30133-2
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