BP神经网络在水下地形高程拟合的应用
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摘要
运用MATLAB软件的神经网络模块建立BP神经网络,以工程河段实测水下地形图中测量点的X、Y坐标值作为输入层神经元,相对应的高程值作为输出层神经元。针对网络训练样本数据较多的特点,对多种训练函数的优劣进行比较,选取适合的训练函数,并进行大量训练实验,不断修正性能参数,并利用水下地形图测量点样本数据进行检验。实验结果表明:该模型对水下地形高程的高精度预测作用满足地形测量工作的工程要求,在实际工程测量中具有很好的应用价值。
A BP neural network was established with neural network module of MATLAB software,and the value of X and Y coordinates of the measured points in the underwater topographic map of the engineering river reach were taken as the input layer neurons,and the corresponding elevation value was the output layer neuron. According to the mass network training sample data,the advantages of various training functions were compared to select the training function suitable for this study,and a large number of training experiments were carried out. The experimental results show that the model can predict the elevation of underwater topography with high accuracy and meet the engineering requirements of topographic survey. So it has a good application value in practical engineering measurement.
A BP neural network was established with neural network module of MATLAB software,and the value of X and Y coordinates of the measured points in the underwater topographic map of the engineering river reach were taken as the input layer neurons,and the corresponding elevation value was the output layer neuron. According to the mass network training sample data,the advantages of various training functions were compared to select the training function suitable for this study,and a large number of training experiments were carried out. The experimental results show that the model can predict the elevation of underwater topography with high accuracy and meet the engineering requirements of topographic survey. So it has a good application value in practical engineering measurement.
引文
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[18]刘品.BP神经网络结构优化研究及应用[D].北京:中国地质大学,2016.LIU Pin. The BP Neural Network Structure Optimization Research and Application[D]. Beijing:China University of Geosciences,2016.
[19]别秀德. BP神经网络应用研究[J].智慧工厂,2016(1):97-102.BIE Xiude. Application researchon BP neural network[J]. Smart Factory,2016(1):97-102.
[2]杨客.遗传算法优化的BP神经网络在连云港港口吞吐量预测中的应用研究[D].深圳:深圳大学,2017.YANG Ke. Research on the Application of BP Neural Network Optimized by Genetic Algorithm in Lianyungang Port Throughput Prediction[D]. Shenzhen:Shenzhen University,2017.
[3] GAN Yong,LIU Xinxin,ZHENG Yuanpan. A research of GPS height fitting in mountainous terrain by CPSO optimization FLS-SVM[J]. Applied Mechanics and Materials,2013,336-338:2339-2343.
[4]WANG Chang. Research on GPS height partition fitting method[J].Applied Mechanics and Materials,2014,580-583:2860-2864.
[5]薛涛,陈辉.高程异常拟合模型在桥梁跨河水准测量中的应用[J].现代交通技术,2015,12(3):44-46.XUE Tao, CHEN Hui. Application of fitting model for height anomaly in bridge river-crossing levelling[J]. Modern Transportation Technology,2015,12(3):44-46.
[6]ZHAO Lingli,LIU Shuai,QUN Cai. The fitting algorithms for GPS height conversion[J]. Applied Mechanics and Materials,2014,543-547:1593-1596.
[7] SHUMYATSKY P,SHALEV A. On the fitting height of a finite group[J]. Journal of Group Theory,2010,13(1):139-142.
[8]CHENG Qianwen,ZHANG Luben,CHEN Honghua. Application of fitting estimation method in GPS height fitting[J]. Advanced Materials Research,2013,694-697:2545-2549.
[9]杨帆,于奇.遗传小波神经网络的GPS高程拟合模型[J].导航定位学报,2017,5(2):131-134.YANG Fan,YU Qi. GPS elevation fitting based on genetic wavelet neural network model[J]. Journal of Navigation and Positioning,2017,5(2):131-134.
[10]简程航.GPS高程拟合方法研究及其工程应用[D].北京:中国地质大学,2014.JIAN Chenghang. GPS Elevation Fitting Method and its Engineering Application[D]. Beijing:China University of Geosciences,2014.
[11]钱建国,刘淑亮.BP神经网络在GPS高程拟合中的应用探讨[J].测绘与空间地理信息,2017,40(1):18-20.QIAN Jianguo, LIU Shuliang. Discussion on application of BP neural network in GPS height fitting[J]. Geomatics&Spatial Information Technology,2017,40(1):18-20.
[12]曾凯,姜岩,吴玮.神经网络在GPS高程拟合中的应用[J].黑龙江工程学院学报(自然科学版),2013,27(3):12-16.ZENG Kai,JIANG Yan,WU Wei. Application of neural network to GPS elevation fitting[J]. Journal of Heilongjiang Institute of Technology(Natural Science Edition),2013,27(3):12-16.
[13]喻宝禄,段迅,吴云. BP神经网络数据预测模型的建立及应用[J].计算机与数字工程,2016,44(3):482-486.YU Baolu,DUAN Xun,WU Yun. Establishment and application of BP neural network data prediction model[J]. Computer and Digital Engineering,2016,44(3):482-486.
[14]吴陈,王和杰.基于改进的自适应遗传算法优化BP神经网络[J].电子设计工程,2016,24(24):29-32.WU Chen,WANG Hejie. BP neural network optimized by improved adaptive genetic algorithm[J]. Electronic Design Engineering,2016,24(24):29-32.
[15]郝海霞.用粒子群算法优化BP神经网络进行函数拟合[J].山西师范大学学报(自然科学版),2017,31(1):14-16.HAO Haixia. Using particle swarm algorithm to optimize BP neural network for function fitting[J]. Journal of Shanxi Normal University(Natural Science Edition),2017,31(1):14-16.
[16]赵会敏,雒江涛,杨军超,等.集成BP神经网络预测模型的研究与应用[J].电信科学,2016,32(2):60-67.ZHAO Huimin,YAN Jiangtao,YANG Junchao,et al. Research and application of integrated BP neural network prediction model[J].Telecommunication Science,2016,32(2):60-67.
[17]盛仲飙. BP神经网络在数据预测中的应用[J].软件导刊,2016,15(1):147-148.SHENG Zhongbiao. Research on BP neural network in data prediction[J]. Software Guide,2016,15(1):147-148.
[18]刘品.BP神经网络结构优化研究及应用[D].北京:中国地质大学,2016.LIU Pin. The BP Neural Network Structure Optimization Research and Application[D]. Beijing:China University of Geosciences,2016.
[19]别秀德. BP神经网络应用研究[J].智慧工厂,2016(1):97-102.BIE Xiude. Application researchon BP neural network[J]. Smart Factory,2016(1):97-102.