基于改进CNN-BP的多波束声纳高程数据预测研究  

Study on Multibeam Sonar Elevation Data Prediction Based on Improved CNN-BP

作　　者：熊豪杰 魏怡[1] XIONG Haojie;WEI Yi(School of Automation,Wuhan University of Technology,Wuhan 430000,China)

机构地区：[1]武汉理工大学自动化学院,武汉430000

出　　处：《计算机科学》2023年第S01期565-568,共4页Computer Science

基　　金：国家自然科学基金(51177114);湖北省技术创新重大专项(2019AAA016)。

摘　　要：为了建立精准的多波束声纳高程数据预测模型,解决人工鱼礁空方量预测准确性的问题,提出了一种基于改进卷积神经网络(Convolutional Neural Network,CNN)和BP神经网络组合模型的多波束声纳高程数据预测方法。首先,利用改进CNN对高程数据进行全卷积操作提取地形趋势特征,再输入到BP中进一步挖掘内部地形趋势变化规律,从而实现多波束声纳高程数据的预测。然后以某海底牧场的多波束声纳高程数据进行实验,并利用人工鱼礁的空方量进行交叉验证。最后,与传统克里金、BP、GA-BP、PSO-BP模型进行比较。结果表明:改进CNN-BP模型在多波束声纳高程数据和人工鱼礁空方量上的预测结果表现最优,验证了该方法的可行性、可靠性和精度高。In order to establish an accurate multibeam sonar elevation data prediction model and solve the problem of the accuracy of air-squared prediction of artificial reefs,a multibeam sonar elevation data prediction method based on a combined model of improved convolutional neural network(CNN)and BP neural network is proposed.First,the improved CNN is used to extract topographic trend features by full convolutional operation of the elevation data,and then input to BP to further explore the internal topographic trend change pattern,so as to achieve the prediction of multibeam sonar elevation data.Experiments are conducted with multibeam sonar elevation data from a submarine ranch and cross-validated using the null square volume of artificial reefs.Finally,it is compared with the traditional kriging,BP,GA-BP,and PSO-BP models.The results show that the improved CNN-BP model performs the best prediction results on multibeam sonar elevation data and artificial reef air-square volume,which verifies the feasibility,reliability and high accuracy of the proposed method.

关 键 词：多波束声纳高程数据 人工鱼礁 卷积神经网络 BP神经网络 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]