Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7257
Title: 基于改进BP神经网络的海底底质分类
Other Titles: Seabed classification with improved BPN neural network
Authors: Tang, QH
Liu, BH
Chen, YQ 
Zhou, XH
Lin, XB
Keywords: Seabed classification
BP classification
GA
Multibeam sounding system
Backscatter strength
Issue Date: 2009
Publisher: 海军海洋测绘研究所
Source: 海洋测绘, Sept. 2009, v. 29, no. 5, p. 40-43, 56
Hydrographic surveying and charting, Sept. 2009, v. 29, no. 5, p. 40-43, 56 How to cite?
Journal: Hydrographic surveying and charting 
Abstract: 通过采用遗传算法优化神经网络初始权值的方法,将GA算法与BP神经网络有机结合,应用于海底底质分类。基于多波束测深系统获取的反向散射强度数据,应用改进的BP神经网络分类方法,实现对海底基岩、砾石、砂、细砂和泥等底质类型的快速、准确识别。通过实验比较,GA-BP神经网络分类精度明显高于BP神经网络,证明了该方法的有效性和可靠性。
Genetic algorithm(GA) is used to optimize the initial values of BP neural network,and GA-BP neural network is applied in the seabed classification.Using the backscatter data from multibeam echo sounder,the improved BP neural network is used to identify all kinds of seabed sediment types,such as rock,gravel,sand,fine sand and mud on the study area.Comparing the classification precisions between GA-BP and BP,the experiment results indicate that the GA-BP neural network approach is effective and reliable in the acoustic seabed classification.
URI: http://hdl.handle.net/10397/7257
ISSN: 1671-3044
Rights: © 2009 中国学术期刊电子杂志出版社。本内容的使用仅限于教育、科研之目的。
© 2009 China Academic Journal Electronic Publishing House. It is to be used strictly for educational and research use.
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