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基于栈式自编码BP神经网络预测水体亚硝态氮浓度模型
引用本文:付泰然,刘广鑫,万全元,吴霆,赵丽娟,林蠡,杨灵.基于栈式自编码BP神经网络预测水体亚硝态氮浓度模型[J].水产学报,2019,43(4):958-967.
作者姓名:付泰然  刘广鑫  万全元  吴霆  赵丽娟  林蠡  杨灵
作者单位:华中农业大学水产学院, 湖北 武汉 430070;仲恺农业工程学院动物科技学院, 广州市水产病害与水禽养殖重点实验室, 广东省水环境与水产品安全工程技术研究中心, 广东省水禽健康养殖重点实验室, 广东 广州 510225,华中农业大学水产学院, 湖北 武汉 430070;仲恺农业工程学院动物科技学院, 广州市水产病害与水禽养殖重点实验室, 广东省水环境与水产品安全工程技术研究中心, 广东省水禽健康养殖重点实验室, 广东 广州 510225,仲恺农业工程学院动物科技学院, 广州市水产病害与水禽养殖重点实验室, 广东省水环境与水产品安全工程技术研究中心, 广东省水禽健康养殖重点实验室, 广东 广州 510225,仲恺农业工程学院信息科学与技术学院, 广东 广州 510225,仲恺农业工程学院动物科技学院, 广州市水产病害与水禽养殖重点实验室, 广东省水环境与水产品安全工程技术研究中心, 广东省水禽健康养殖重点实验室, 广东 广州 510225,仲恺农业工程学院动物科技学院, 广州市水产病害与水禽养殖重点实验室, 广东省水环境与水产品安全工程技术研究中心, 广东省水禽健康养殖重点实验室, 广东 广州 510225,仲恺农业工程学院信息科学与技术学院, 广东 广州 510225
基金项目:广东省高等教育“创新强校工程”专项(KA170500G);广州市民生科技攻关计划(201803020033,201704020030)
摘    要:亚硝态氮对于水产养殖动物具有毒性,对于其含量的及时监控非常重要。基于光谱法和电极法设计的亚硝态氮传感器价格昂贵,难以大面积推广,因此急需研发一种能快速预测养殖水体亚硝态氮的模型。实验通过实验室构建的水质在线检测系统测定水体中温度、pH、溶解氧、氧化还原电位4个参数,同时用α-萘胺比色法测定水体中亚硝态氮的浓度,从4种参数中选取与亚硝态氮浓度相关的参数作为预测模型的关联变量。水质参数数据及亚硝态氮浓度数据分别经预处理后作为原始数据用于SAE神经网络的训练,训练方法采用无监督逐层贪婪训练法,用学习到的特征监督训练SAE-BP神经网络,利用反向传播算法(BP)优化模型。训练得到结构为4-5-4-3-1的SAE-BP神经网络模型,建立的神经网络模型对实验数据预测的拟合优度R2为0.95,预测结果的均方根误差RMSEP为0.099 71。研究表明,亚硝态氮预测模型可以较为精准地预测水体中亚硝态氮的浓度。本模型将为开发在线快速监测养殖水体亚硝态氮浓度提供新的思路。

关 键 词:亚硝态氮  栈式自编码  SAE-BP神经网络  预测模型
收稿时间:2018/4/25 0:00:00
修稿时间:2018/9/26 0:00:00

Establishment of a water nitrite nitrogen concentration prediction modelbased on stacked autoencoder-BP neural network
FU Tairan,LIU Guangxin,WAN Quanyuan,WU Ting,ZHAO Lijuan,LIN Li and YANG Ling.Establishment of a water nitrite nitrogen concentration prediction modelbased on stacked autoencoder-BP neural network[J].Journal of Fisheries of China,2019,43(4):958-967.
Authors:FU Tairan  LIU Guangxin  WAN Quanyuan  WU Ting  ZHAO Lijuan  LIN Li and YANG Ling
Institution:College of Fisheries, Huazhong Agricultural University, Wuhan 430070, China;Guangzhou Key Laboratory of Aquatic Animal Diseases and Waterfowl Breeding, Guangdong Provincial Water Environment and Aquatic Products Security Engineering Technology Research Center, Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Sciences and Technology,Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China,College of Fisheries, Huazhong Agricultural University, Wuhan 430070, China;Guangzhou Key Laboratory of Aquatic Animal Diseases and Waterfowl Breeding, Guangdong Provincial Water Environment and Aquatic Products Security Engineering Technology Research Center, Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Sciences and Technology,Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China,Guangzhou Key Laboratory of Aquatic Animal Diseases and Waterfowl Breeding, Guangdong Provincial Water Environment and Aquatic Products Security Engineering Technology Research Center, Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Sciences and Technology,Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China,College of Information Science and Technology, Zhongkai University of Agriculture and Engineering,Guangzhou 510225, China,Guangzhou Key Laboratory of Aquatic Animal Diseases and Waterfowl Breeding, Guangdong Provincial Water Environment and Aquatic Products Security Engineering Technology Research Center, Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Sciences and Technology,Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China,Guangzhou Key Laboratory of Aquatic Animal Diseases and Waterfowl Breeding, Guangdong Provincial Water Environment and Aquatic Products Security Engineering Technology Research Center, Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Sciences and Technology,Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China and College of Information Science and Technology, Zhongkai University of Agriculture and Engineering,Guangzhou 510225, China
Abstract:Nitrite nitrogen is toxic to the aquatic animals. Monitoring the concentration of nitrite nitrogen is very critical for the culture of aquatic animals. Due to the high cost of the current commercial electrode sensor which is used to measure the concentration of nitrite nitrogen in water, this kind of sensor is very difficult to be popularized on a large scale. Therefore, it is an urgent need to develop another novel method to predict the concentration of nitrite nitrogen in water. In this paper, taking the advantage of the established online water monitoring system in our laboratory, the temperature, pH value, dissolved oxygen and oxidation-reduction potential were recorded from the water in tanks. Meanwhile, the actual concentration of nitrate nitrogen in water was measured using alpha-naphthalene colorimetric method. The data after pretreatment were used as the original data to be used for SAE neural network training. Thereafter, unsupervised greed training method was applied. The learnt characteristics were used for the supervision and training of BP neural network. The model was optimized using the back propagation (BP) algorithm. The prediction model R2 of the nitrite nitrogen after training was 0.95, and root mean square error of the prediction (RMSEP) was 0.099 71, indicating that the model could accurately predict the concentrations of nitrate nitrogen in water. The established model will pave a new way for developing online system for monitoring the water nitrate nitrogen concentration in the future.
Keywords:nitrate nitrogen  stacked autoencoder  SAE-BP neural network  prediction model
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