基于BP神经网络的虾蛄捕捞海域溯源方法
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摘要
虾蛄是重要的经济水产品,对环境中重金属元素具有富集作用。不同海域捕捞的虾蛄体内重金属含量差异较大,因此,可根据虾蛄体内重金属含量推断捕捞海域的污染状况,即基于虾蛄体内重金属状况对其来源进行溯源。以渤海、东海和南海三大海域虾蛄中的3种重金属含量数据作为输入,建立BP神经网络判别分析模型,并对模型进行优化,通过模型判断虾蛄样本的来源海域。结果显示:经过网络训练后,总计90个样本中,86个分类正确,模型的判别准确率为95.6%,其中训练集判别准确率为98.1%,验证集准确率为94.4%,测试集准确率为88.9%。研究表明:基于BP神经网络建立的判别分析模型能够解析非线性复杂体系中各元素的内在关联,以区分样品的来源,并可据此进行有效的追溯。
Mantis shrimp is one of the important economic aquatic products in China,which enriches the heavy metal elements in the environment. There is a significant difference in contents of heavy metal elements among mantis shrimps in different sea areas. And thus that the pollution status of fishing sea areas could be inferred according to the contents of heavy metal elements in the corresponding mantis shrimps. That is to say,the origin sea area of shrimp mantis can be traced by the contents of heavy metal elements in it. The data of the contents of the three kinds of heavy metals in mantis shrimps from the corresponding three sea areas were used as the input to create a BP neural network discriminant analysis model,which can discriminate the origin sea area of the corresponding mantis shrimp samples after the well optimization of the models. The result showed,using the trained neural network,86 samples among a total of 90 can be discriminated correctly,the accuracy rate is 95.6%,of which,the accuracy rate by using the training sets is 98.1%,the accuracy rate by using the validation set is 94. 4%,and the accuracy rate by using the test set is 88. 9%. The results showed that the discriminatory analysis model based on BP neural network model can be used to analyses the relationship among the different elements with the nonlinear complex systems,so as to discriminate the samples and trace them effectively.
Mantis shrimp is one of the important economic aquatic products in China,which enriches the heavy metal elements in the environment. There is a significant difference in contents of heavy metal elements among mantis shrimps in different sea areas. And thus that the pollution status of fishing sea areas could be inferred according to the contents of heavy metal elements in the corresponding mantis shrimps. That is to say,the origin sea area of shrimp mantis can be traced by the contents of heavy metal elements in it. The data of the contents of the three kinds of heavy metals in mantis shrimps from the corresponding three sea areas were used as the input to create a BP neural network discriminant analysis model,which can discriminate the origin sea area of the corresponding mantis shrimp samples after the well optimization of the models. The result showed,using the trained neural network,86 samples among a total of 90 can be discriminated correctly,the accuracy rate is 95.6%,of which,the accuracy rate by using the training sets is 98.1%,the accuracy rate by using the validation set is 94. 4%,and the accuracy rate by using the test set is 88. 9%. The results showed that the discriminatory analysis model based on BP neural network model can be used to analyses the relationship among the different elements with the nonlinear complex systems,so as to discriminate the samples and trace them effectively.
引文
[1]孙珊,刘霞,谷伟丽,等.虾蛄对石油烃的生物富集动力学及安全限量[J].海洋环境科学,2014,33(3):361-365.
[2]农业部渔业渔政管理局.2016中国渔业统计年鉴[M].北京:中国农业出版社,2016:44.
[3]颜静,唐成,梁亚雄,等.柚子原产地溯源鉴定技术[J].食品科学,2014,35(4):248-252.
[4]吴岸成.神经网络与深度学习[M].北京:电子工业出版社,2016:66.
[5]许禄,邵学广.化学计量学方法[M].北京:科学出版社,2004:290.
[6]刘冰,郭海霞.MATLAB神经网络超级学习手册[M].北京:人民邮电出版社,2014:159.
[7]袁红春,胡倩倩,沈晓倩,等.基于神经网络的规则提取及其渔业应用研究[J].海洋科学,2014,38(9):79-84.
[8]邵磊,肖志忠等.LM神经网络在鱼类胚胎保存抗冻剂毒性试验设计中的应用[J].实验与技术,2007.31(7):1-3,24.
[9]汪翔,何吉祥,佘磊,等.基于NAR神经网络对养殖水体亚硝酸盐预测模型的研究[J].渔业现代化,2015.42(4):30-34.
[10]堵锡华,朱锦锦,周荣婷,等.神经网络法应用于黄烷酮衍生物抗菌活性的理论研究[J].徐州工程学院学报(自然科学版),2016,31(3):17-21.
[11]李燕军,朱建华,王万历,等.MATLAB在测绘数据处理中的优越性及应用[J].甘肃科技,2016,32(4):18-20.
[12]王新安,马爱军,赵艳飞,等.基于径向基函数(RBF)神经网络的红鳍东方鲀体质量预测[J].水产学报,2015,39(2):1799-1805.
[13]徐大明,周超,孙传恒,等.基于粒子群优化BP神经网络的水产养殖水温及p H预测模型[J].渔业现代化,2016,43(1):24-29.
[14]宋协法,马真,万荣,等.人工神经网络在凡纳滨对虾养殖水质预测中的应用研究[J].中国海洋大学学报,2014,44(6):028-033.
[15]郑建安.主成分和BP神经网络在粮食产量预测中的组合应用[J].计算机系统应用,2016,25(11):274-278.
[16]张玉荣,付玲,周显青.基于BP神经网络小麦含水量的近红外检测方法[J].河南工业大学学报(自然科学版),2013,34(1):17-20.
[17]王海英,曹晶,谢骏,等.基于L-M神经网络优化算法的池塘水色判别系统的初步建立[J].渔业现代化,2010,37(5):19-21,37.
[18]楼文高.人工神经网络在水产科学中的应用[J].上海水产大学学报,2001,10(4):347-352.
[19]王俊,刘明哲,庹先国,等.遗传算法优化的BP神经网络在EDXRF中对钛铁元素含量的预测[J].原子能科学技术,2015,49(6):1143-1148.
[20]安生梅,吴启勋,吉守祥,等.基于近红外光谱概率神经网络的诃子产地鉴别[J].湖北农业科学,2014,53(20):4977-4979.
[21]ZOU CHUN FANG,HUANG LI JUAN.A new BP algorithm for network marketing performance evaluation of agricultural products[J].Journal of Applied sciences,2013,13(20):4332-4335.
[22]MO LIAN GUANG,XIE ZHENG.An improved BP neural network based on IPSO and its application[J].Journal of computers,2013,8(5):1267-1272.
[23]XIE LING.The heat load prediction model based on BP neutral network-Markov model[J].Procedia computer science,2017,107:296-300.
[24]ZHU QUAN YIN,PAN LU,YIN YONG HUA,et al.Influence on normalization and magnitude normalization for price forecasting of agricultural products[J].information technology journal,2013,12(15):3046-3057.
[2]农业部渔业渔政管理局.2016中国渔业统计年鉴[M].北京:中国农业出版社,2016:44.
[3]颜静,唐成,梁亚雄,等.柚子原产地溯源鉴定技术[J].食品科学,2014,35(4):248-252.
[4]吴岸成.神经网络与深度学习[M].北京:电子工业出版社,2016:66.
[5]许禄,邵学广.化学计量学方法[M].北京:科学出版社,2004:290.
[6]刘冰,郭海霞.MATLAB神经网络超级学习手册[M].北京:人民邮电出版社,2014:159.
[7]袁红春,胡倩倩,沈晓倩,等.基于神经网络的规则提取及其渔业应用研究[J].海洋科学,2014,38(9):79-84.
[8]邵磊,肖志忠等.LM神经网络在鱼类胚胎保存抗冻剂毒性试验设计中的应用[J].实验与技术,2007.31(7):1-3,24.
[9]汪翔,何吉祥,佘磊,等.基于NAR神经网络对养殖水体亚硝酸盐预测模型的研究[J].渔业现代化,2015.42(4):30-34.
[10]堵锡华,朱锦锦,周荣婷,等.神经网络法应用于黄烷酮衍生物抗菌活性的理论研究[J].徐州工程学院学报(自然科学版),2016,31(3):17-21.
[11]李燕军,朱建华,王万历,等.MATLAB在测绘数据处理中的优越性及应用[J].甘肃科技,2016,32(4):18-20.
[12]王新安,马爱军,赵艳飞,等.基于径向基函数(RBF)神经网络的红鳍东方鲀体质量预测[J].水产学报,2015,39(2):1799-1805.
[13]徐大明,周超,孙传恒,等.基于粒子群优化BP神经网络的水产养殖水温及p H预测模型[J].渔业现代化,2016,43(1):24-29.
[14]宋协法,马真,万荣,等.人工神经网络在凡纳滨对虾养殖水质预测中的应用研究[J].中国海洋大学学报,2014,44(6):028-033.
[15]郑建安.主成分和BP神经网络在粮食产量预测中的组合应用[J].计算机系统应用,2016,25(11):274-278.
[16]张玉荣,付玲,周显青.基于BP神经网络小麦含水量的近红外检测方法[J].河南工业大学学报(自然科学版),2013,34(1):17-20.
[17]王海英,曹晶,谢骏,等.基于L-M神经网络优化算法的池塘水色判别系统的初步建立[J].渔业现代化,2010,37(5):19-21,37.
[18]楼文高.人工神经网络在水产科学中的应用[J].上海水产大学学报,2001,10(4):347-352.
[19]王俊,刘明哲,庹先国,等.遗传算法优化的BP神经网络在EDXRF中对钛铁元素含量的预测[J].原子能科学技术,2015,49(6):1143-1148.
[20]安生梅,吴启勋,吉守祥,等.基于近红外光谱概率神经网络的诃子产地鉴别[J].湖北农业科学,2014,53(20):4977-4979.
[21]ZOU CHUN FANG,HUANG LI JUAN.A new BP algorithm for network marketing performance evaluation of agricultural products[J].Journal of Applied sciences,2013,13(20):4332-4335.
[22]MO LIAN GUANG,XIE ZHENG.An improved BP neural network based on IPSO and its application[J].Journal of computers,2013,8(5):1267-1272.
[23]XIE LING.The heat load prediction model based on BP neutral network-Markov model[J].Procedia computer science,2017,107:296-300.
[24]ZHU QUAN YIN,PAN LU,YIN YONG HUA,et al.Influence on normalization and magnitude normalization for price forecasting of agricultural products[J].information technology journal,2013,12(15):3046-3057.