Paper Title:
Classification of Underwater Echo Based on Fractal Theory and Learning Vector Quantization Neural Network
| Periodical | Applied Mechanics and Materials (Volumes 148 - 149) |
|---|---|
| Main Theme | Mechanical Engineering, Materials and Energy |
| Edited by | Grace Chang |
| Pages | 1365-1369 |
| DOI | 10.4028/www.scientific.net/AMM.148-149.1365 |
| Citation | Pu Hua Tang et al., 2011, Applied Mechanics and Materials, 148-149, 1365 |
| Online since | December, 2011 |
| Authors | Pu Hua Tang, Mu Rong Zhou, Ying Yong Bu |
| Keywords | Classify, Continuous Wavelet Transform, Fractal, Learning Vector Quantization (LVQ) Neural Network |
| Price | US$ 28,- |
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Abstract
A classification method for underwater echo is introduced, which based on fractal theory and learning vector quantization (LVQ) neural network. The fractal dimension was extracted from the underwater echo by continuous wavelet transform. Combining with accumulative energy as input of a LVQ neural network, neural network was used to classify four kinds of underwater echo. The experimental results showed this method is effective and reliable.
