BP和RBF神经网络预测射流式喷头射程对比
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摘要
为了探究适合全射流喷头多因素下射程的预测模型,通过改变喷头工作压力、安装高度、喷嘴直径、喷头仰角共4个参数,对射程进行测量.基于BP神经网络和广义径向基(RBF)神经网络的基本原理和算法,建立了全射流喷头射程预测的BP和RBF神经网络模型,并分析BP和RBF神经网络的预测性能.结果表明射程与工作压力、喷嘴直径呈非线性关系;当喷头在1.2 m安装高度、27°仰角、4～10 mm喷嘴直径时,压力增大到0.4 MPa,射程趋于极限,并且安装高度与射程呈正相关关系.BP与RBF神经网络均能较好地表达全射流喷头射程与主控因素之间的非线性关系.在训练时间方面,RBF网络比BP网络慢8.05 s;预测过程中,BP网络在每次运行程序时的预测结果不一定相同,而RBF网络则不会出现此问题,且RBF网络预测值与实测值之间的平均绝对误差比BP网络的小3.55%.从网络预测总体效果观察,RBF神经网络预测喷头射程具有更好的推广能力.
To explore a suitable multi-parameter model for predicting ranges of completely fluidic sprinkler,the ranges of a fluidic sprinkler were measured by varying four parameters such as installation height,diameter,nozzle tilt angle and working pressure. Based on the basic principles and algorithms of BP neural network and generalized radial basis( RBF) neural network,BP and RBF neural network models for range prediction of the fluidic sprinkler were established to analyze the prediction performance. The results show that the range is related nonlinearly to the working pressure and nozzle diameter. For the nozzle installed at 1.2 m height,27° tilt angle and with a diameter ranged in 4-10 mm,the range tends to reach a limit with the working pressure increased to 0.4 MPa,and the installation height is correlated positively with the range. Both BP and RBF neural networks can better express the nonlinear relationship between the range and the main control parameters of the fluidic sprinkler. In terms of training time,the RBF network is 8. 05 s longer than the BP network. In the prediction process,the BP network program can produce the same result unnecessarily in each run,however,this problem doesn't exist in the RBF network. Further,the average prediction error of the RBF network against the measurements is 3.55% smaller than that of the BP network. Based on the overall effectiveness of both networks in prediction,the nozzle range predicted by RBF neural network model has an even better applicability.
To explore a suitable multi-parameter model for predicting ranges of completely fluidic sprinkler,the ranges of a fluidic sprinkler were measured by varying four parameters such as installation height,diameter,nozzle tilt angle and working pressure. Based on the basic principles and algorithms of BP neural network and generalized radial basis( RBF) neural network,BP and RBF neural network models for range prediction of the fluidic sprinkler were established to analyze the prediction performance. The results show that the range is related nonlinearly to the working pressure and nozzle diameter. For the nozzle installed at 1.2 m height,27° tilt angle and with a diameter ranged in 4-10 mm,the range tends to reach a limit with the working pressure increased to 0.4 MPa,and the installation height is correlated positively with the range. Both BP and RBF neural networks can better express the nonlinear relationship between the range and the main control parameters of the fluidic sprinkler. In terms of training time,the RBF network is 8. 05 s longer than the BP network. In the prediction process,the BP network program can produce the same result unnecessarily in each run,however,this problem doesn't exist in the RBF network. Further,the average prediction error of the RBF network against the measurements is 3.55% smaller than that of the BP network. Based on the overall effectiveness of both networks in prediction,the nozzle range predicted by RBF neural network model has an even better applicability.
引文
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[11]张宏,马岩,李勇,等.基于BP神经网络的核桃破裂功预测模型[J].农业工程学报,2014,30(18):78-84.ZHANG Hong,MA Yan,LI Yong,et al.Rupture energy prediction model for walnut shell breaking based on genetic BP neural network[J].Transactions of the CSAE,2014,30(18):78-84.(in Chinese)
[12]涂琴,李红,王新坤,等.不同指标轻小型喷灌机组配置优化[J].农业工程学报,2013,29(22):83-89.TU Qin,LI Hong,WANG Xinkun,et al.Optimization of small-scale sprinkler irrigation systems for different indicators[J].Transactions of the CSAE,2013,29(22):83-89.(in Chinese)
[13]王波雷,马孝义,郝晶晶.基于L-M优化算法的喷头射程神经网络预测模型[J].农业机械学报,2008,39(5):36-40,35.WANG Bolei,MA Xiaoyi,HAO Jingjing.Prediction of sprinkler nozzle range based on L-M optimized algorithm[J].Transactions of the CSAM,2008,39(5):36-40,35.(in Chinese)
[14]楼文高,刘遂庆.区域水资源可持续利用评价的神经网络方法[J].农业系统科学与综合研究,2004,20(2):113-119.LOU Wengao,LIU Suiqing.On assessment of sustainable development level of regional water resource using artificial neural networks[J].System sciences and comprehensive studies in agriculture,2004,20(2):113-119.(in Chinese)
[15]MOODY J,DARKEN C J.Fast learning in networks of locally-tuned processing units[J].Neural computation,2014,1(2):281-294.
[16]COUREY A J,HOLTZMAN D A,JACKSON S P,et al.Syner-gistic activation by the glutamine-rich domains of human transcription factor Sp1[J].Cell,1989,59(5):827-836.
[17]刘兴发.全射流喷头水滴分布特性试验研究[D].镇江:江苏大学,2016.
[18]刘俊萍,袁寿其,李红,等.全射流喷头射程与喷洒均匀性影响因素分析与试验[J].农业机械学报,2008,39(11):51-54.LIU Junping,YUAN Shouqi,LI Hong,et al.Analysis and experiment on influencing factors of range and spraying uniformity for complete fluidic sprinkler[J].Transactions of the CSAM,2008,39(11):51-54.(in Chinese)
[19]任自中.柴油机相继增压系统的理论与试验研究[J].内燃机工程,2001(1):32-37.REN Zizhong.Theoretical and experimental study on the sequential turbocharging system of diesel engines[J].Chinese internal combustion engine engineering,2001(1):32-37.(in Chinese)
[2]陆梦雅,陆科骥,胡广,等.SD-03型地埋式喷头水力性能试验研究[J].排灌机械工程学报,2018,36(11):1120-1124.LU Mengya,LU Keji,HU Guang,et al.Expe-riment on hydraulic performance of type SD-03 pop-up sprinkler[J].Journal of drainage and irrigation machinery engineering,2018,36(11):1120-1124.(in Chinese)
[3]EDLING R J.Kinetic energy,evaporation and wind drift of droplet from low pressure irrigation nozzles[J].Transac-tion of the ASAE,1985,28(5):1543-1550.
[4]干浙民,杨生华.旋转式喷头射程的试验研究及计算公式[J].农业机械学报,1998,29(4):145-149.GAN Zhemin,YANG Shenghua.The formula and experimental research on the range of the whirl sprinkler[J].Transactions of the CSAM,1998,29(4):145-149.(in Chinese)
[5]DE LIMA J L M P,TORFS P J J F,SINGH V P.Amathematical model for evaluating the effect of wind on downward-spraying rainfall simulators[J].Catena,2002,46(4):221-241.
[6]脱云飞,杨路华,柴春岭,等.喷头射程理论公式与试验研究[J].农业工程学报,2006,22(1):23-26.TUO Yunfei,YANG Luhua,CHAI Chunling,et al.Experimental study and theoretical formula of the sprinkler range[J].Transactions of the CSAE,2006,22(1):23-26.(in Chinese)
[7]吕名礼,张中华,刘俊萍.微喷头水力性能及喷灌组合均匀性试验研究[J].排灌机械工程学报,2017,35(7):641-644.LYU Mingli,ZHANG Zhonghua,LIU Junping.Experimental study on hydraulic performance and combined uniformity of micro sprinklers[J].Journal of drainage and irrigation machinery engineering,2017,35(7):641-644.(in Chinese)
[8]刘敏华.BP网络模型在大型泵站用电量预测中的应用[J].水电能源科学,2004,22(1):86-88.LIU Minhua.Application of BP model to power consumption forecast in big pump station[J].Water resources and power,2004,22(1):86-88.(in Chinese)
[9]王晓玲.基于DEBBO-LM算法的BP网络非线性系统辨识[D].成都:西南交通大学,2012.
[10]陈光华.人工神经网络在证券价格预测中的应用[J].计算机仿真,2007,24(10):244-248.CHEN Guanghua.Application of artificial neural networks in price forecast of securities market[J].Computer simulation,2007,24(10):244-248.(in Chinese)
[11]张宏,马岩,李勇,等.基于BP神经网络的核桃破裂功预测模型[J].农业工程学报,2014,30(18):78-84.ZHANG Hong,MA Yan,LI Yong,et al.Rupture energy prediction model for walnut shell breaking based on genetic BP neural network[J].Transactions of the CSAE,2014,30(18):78-84.(in Chinese)
[12]涂琴,李红,王新坤,等.不同指标轻小型喷灌机组配置优化[J].农业工程学报,2013,29(22):83-89.TU Qin,LI Hong,WANG Xinkun,et al.Optimization of small-scale sprinkler irrigation systems for different indicators[J].Transactions of the CSAE,2013,29(22):83-89.(in Chinese)
[13]王波雷,马孝义,郝晶晶.基于L-M优化算法的喷头射程神经网络预测模型[J].农业机械学报,2008,39(5):36-40,35.WANG Bolei,MA Xiaoyi,HAO Jingjing.Prediction of sprinkler nozzle range based on L-M optimized algorithm[J].Transactions of the CSAM,2008,39(5):36-40,35.(in Chinese)
[14]楼文高,刘遂庆.区域水资源可持续利用评价的神经网络方法[J].农业系统科学与综合研究,2004,20(2):113-119.LOU Wengao,LIU Suiqing.On assessment of sustainable development level of regional water resource using artificial neural networks[J].System sciences and comprehensive studies in agriculture,2004,20(2):113-119.(in Chinese)
[15]MOODY J,DARKEN C J.Fast learning in networks of locally-tuned processing units[J].Neural computation,2014,1(2):281-294.
[16]COUREY A J,HOLTZMAN D A,JACKSON S P,et al.Syner-gistic activation by the glutamine-rich domains of human transcription factor Sp1[J].Cell,1989,59(5):827-836.
[17]刘兴发.全射流喷头水滴分布特性试验研究[D].镇江:江苏大学,2016.
[18]刘俊萍,袁寿其,李红,等.全射流喷头射程与喷洒均匀性影响因素分析与试验[J].农业机械学报,2008,39(11):51-54.LIU Junping,YUAN Shouqi,LI Hong,et al.Analysis and experiment on influencing factors of range and spraying uniformity for complete fluidic sprinkler[J].Transactions of the CSAM,2008,39(11):51-54.(in Chinese)
[19]任自中.柴油机相继增压系统的理论与试验研究[J].内燃机工程,2001(1):32-37.REN Zizhong.Theoretical and experimental study on the sequential turbocharging system of diesel engines[J].Chinese internal combustion engine engineering,2001(1):32-37.(in Chinese)