| 栉孔扇贝夏季大规模死亡的神经网络预测模型 |
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| 引用本文： | 邓登,麦康森,李晓明,马洪明,谭北平.栉孔扇贝夏季大规模死亡的神经网络预测模型[J].水产学报,2009,33(3):438-444. |
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| 作者姓名： | 邓登 麦康森 李晓明 马洪明 谭北平 |
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| 作者单位： | 1. 中国海洋大学教育部海水养殖重点实验室,山东,青岛,266003
2. 中国海洋大学海洋信息科学与工程学院,山东,青岛,266003 |
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| 基金项目： | 国家重点基础研究发展规划(973计划) |
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| 摘 要： | 根据2002年和2003年对山东荣成桑沟湾栉孔扇贝养殖海区的水温、盐度、pH、氨氮浓度、亚硝氮浓度等环境因子和扇贝血清中的蛋白浓度、酸性磷酸酶活力、碱性磷酸酶活力、超氧化物歧化酶活力和过氧化氢酶活力等免疫学指标及栉孔扇贝养殖密度和死亡率的监测数据,运用人工神经网络(artificial neurd network,ANN)的原理和误差反相传播(back propagefion,BP)网络的方法,利用MATLAB软件初步建立养殖栉孔扇贝夏季大规模死亡的BP人工神经网络预测模型.预测模型经过300次的学习训练,误差平方和由67.46下降至0.009 1.该预测模型对未参与模型构建的样本预测的结果与实际监测结果的符合率达到87.5%.首次将人工神经网络与水产动物病害死亡的预测相结合,建立的预测模型具有对数据适应能力强,可适时学习,预测结果准确等突出优点,为水产养殖动物病害死亡程度的预测提供了一个新的研究方法.
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| 关 键 词： | 栉孔扇贝 大规模死亡 人工神经网络 预测模型 |
| 收稿时间： | 2008/4/18 0:00:00 |
| 修稿时间： | 2008/10/5 0:00:00 |
Prediction model (ANN) for massive death of scallop Chlamys farreri in summer |
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| DENG Deng,MAI Kang-seng,LI Xiao-ming,MA Hong-ming and TAN Bei-ping.Prediction model (ANN) for massive death of scallop Chlamys farreri in summer[J].Journal of Fisheries of China,2009,33(3):438-444. |
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| Authors: | DENG Deng MAI Kang-seng LI Xiao-ming MA Hong-ming and TAN Bei-ping |
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| Affiliation: | Shenzhen Alpha Feed Group, Ltd. |
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| Abstract: | The mass mortality of cultured scallops Chlamys farreri often occurs in summer and brings huge loss to farmers. However, the loss could be reduced greatly by transferring and renewing scallop cages or harvesting scallops before the occurrence of the massive death of scallops. A model for predicting the death of scallops by using the principle of Artificial Neural Network (ANN), the method of Back Propagation (BP) network and MATLAB software has been developed. The data to build the prediction model were acquired from a two year (2002 & 2003) investigation on temperature, salinity, pH, NH3-N, NO2-N of seawater and protein concentration, acid phosphatase activity, alkaline phosphatase activity, superioxide dismutase activity, catalase activity of scallops serum in Sanggou Bay, Rongcheng, Shandong Province. We debugged the model repeatedly by changing the key parameters, input layer node number, hidden layer node number, sample number and epoch number. After 300 times of studies and training, the sum squared error of the prediction model decreased from 67.46 to 0.009 1. The model was tested, and the prediction accuracy was 87.5%. It is the first time that ANN was used in the aquaculture disease prediction. This model has many strong points, such as data adapting well, study momentarily and predicting accurately. The present study presents a new way for disease prediction and control of aquaculture animals. |
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| Keywords: | Chlamys farreri massive death artificial neural network prediction model |
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