结合遗传算法的LVQ神经网络在声学底质分类中的应用

Abstract

學習向量量化 (Learning Vector Quantization,LVQ) 神經網絡在聲學底質分類中具有廣泛應用. 常用的LVQ神經網絡存在神經元未被充分利用以及算法對初值敏感的問題,影響底質分類精度. 本文提出采用遺傳算法 (Genetic Algorithms,GA) 優化神經網絡的初始值,將GA與LVQ神經網絡結合起來,迅速得到最佳的神經網絡初始權值向量,實現對海底基巖、礫石、砂、細砂以及泥等底質類型的快速、準確識別.將其應用于青島膠州灣海區底質分類識別研究中,通過與標準的LVQ神經網絡的分類結果進行比較表明,該方法在分類速度以及精度上都有了較大提高. 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.