%0 Journal Article
%T Application of LVQ neural network combined with the genetic algorithm in acoustic seafloor classification<br>结合遗传算法的LVQ神经网络在声学底质分类中的应用
%A TANG Qiu_Hua
%A LIU Bao_Hua
%A CHEN Yong_Qi
%A ZHOU Xing_Hua
%A DING Ji_Sheng
%A <br>唐秋华
%A 刘保华
%A 陈永奇
%A 周兴华
%A 丁继胜
%J 地球物理学报
%D 2007
%I 
%X 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.
%K Learning Vector Quantization(LVQ)
%K Genetic Algorithm(GA)
%K Multibeam echo sounder
%K Seafloor classification<br>学习向量量化
%K 遗传算法
%K 多波束测深系统
%K 底质分类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=1E44AE713D8A6DE0&jid=14DC41C59CBF6770055A7D610D53AE46&aid=3460ECFC11228449&yid=A732AF04DDA03BB3&vid=771152D1ADC1C0EB&iid=CA4FD0336C81A37A&sid=C2F76551C0111538&eid=160561E9A96393DE&journal_id=0001-5733&journal_name=地球物理学报&referenced_num=3&reference_num=25