| 结合遗传算法的LVQ神经网络在声学底质分类中的应用 |
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| 引用本文： | 唐秋华,刘保华,陈永奇,周兴华,丁继胜.结合遗传算法的LVQ神经网络在声学底质分类中的应用[J].地球物理学报,2007,50(1):313-319. |
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| 作者姓名： | 唐秋华 刘保华 陈永奇 周兴华 丁继胜 |
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| 作者单位： | 1.中国海洋大学海洋地球科学学院, 青岛266003 2 国家海洋局第一海洋研究所, 青岛266061 3 香港理工大学土地测量及地理资讯学系, 香港 |
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| 基金项目： | 国家高技术研究发展计划(863计划)，香港研究资助局资助项目 |
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| 摘 要： | 学习向量量化（Learning Vector Quantization，LVQ）神经网络在声学底质分类中具有广泛应用. 常用的LVQ神经网络存在神经元未被充分利用以及算法对初值敏感的问题，影响底质分类精度. 本文提出采用遗传算法（Genetic Algorithms，GA）优化神经网络的初始值，将GA与LVQ神经网络结合起来，迅速得到最佳的神经网络初始权值向量，实现对海底基岩、砾石、砂、细砂以及泥等底质类型的快速、准确识别. 将其应用于青岛胶州湾海区底质分类识别研究中，通过与标准的LVQ神经网络的分类结果进行比较表明，该方法在分类速度以及精度上都有了较大提高.
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| 关 键 词： | 学习向量量化 遗传算法 多波束测深系统 底质分类 |
| 文章编号： | 0001-5733（2007）01-0313-07 |
| 收稿时间： | 2005-12-10 |
| 修稿时间： | 2005-12-10 |
Application of LVQ neural network combined with the genetic algorithm in acoustic seafloor classification |
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| TANG Qiu-Hua,LIU Bao-Hua,CHEN Yong-Qi,ZHOU Xing-Hua,DING Ji-Sheng.Application of LVQ neural network combined with the genetic algorithm in acoustic seafloor classification[J].Chinese Journal of Geophysics,2007,50(1):313-319. |
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| Authors: | TANG Qiu-Hua LIU Bao-Hua CHEN Yong-Qi ZHOU Xing-Hua DING Ji-Sheng |
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| Institution: | 1.Marine Geology College, Ocean University of China, Qingdao 266003, China 2 First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China 3 Department of Land Surveying and Geo_Informatics, The Hong Kong Polytechnic University, Hong Kong, China |
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| Abstract: | The Learning Vector Quantization(LVQ) neural network approach has been widely used in acoustic seafloor classification.However,one of the major weak points of LVQ is its sensitivity to the initialization,affecting the seafloor classification accuracy.In this paper,Genetic Algorithm(GA) is used to optimize the initial values of LVQ.The GA-based LVQ can rapidly provide the most optimized initial reference vectors and accurately identify many types of seafloor,such as rock,gravel,sand,fine sand and mud in survey areas.The proposed new approach has been applied to seafloor classification using Multibeam Echo Sounder(MBES) backscatter data in Jiaozhou Bay near Qingdao City of China. Comparing the evolving LVQ with the standard LVQ,the experiment results indicate that the approach of GA-based LVQ has improved the seafloor classification speed and accuracy. |
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| Keywords: | Learning Vector Quantization(LVQ) Genetic Algorithm(GA) Multibeam echo sounder Seafloor classification |
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