Classification of Underwater Echo Based on Fractal Theory and Learning Vector Quantization Neural Network

摘要：

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.

关键词： fractal learning vector quantization (LVQ) neural network classify continuous wavelet transform.

作者: Pu-hua Tang Mu-rong Zhou Ying-yong Bu

作者单位: Department of Mechanical and Electrical Engineering, Changsha College, Changsha Hunan 410003, China Lingling Cigarette Factory, China Tobacco Hunan Industrial Company Ltd, Yongzhou Hunan 425002, China College of Mechanical and Electrical Engineering, Central South University, Changsha Hunan 410083, C

会议类型: 国际会议

会议名称: 2011 International Conference on Machanical Engineering,Materials and Energy(2011年机械工程、材料与能源国际会议 ICMEME 2011)

会议地点: 大连

会议语种:英文

页码: 1365-1369

在线出版日期: 2011-10-19（万方平台首次上网日期，不代表论文的发表时间）