基于GA-BP神经网络的池塘养殖水温短期预测系统
发布时间：2018-09-19 14:00
【摘要】：为解决传统的水温小样本非实时预测方法预测精度低、鲁棒性差等问题,基于物联网实时数据,提出了遗传算法(GA)优化BP神经网络的池塘养殖水温短期预测方法,并在此基础上设计开发了池塘养殖水温预测系统,首先采用主成分分析法筛选出影响池塘水温的关键影响因子,减少输入元素;然后使用遗传算法对初始权重和阈值进行优化,获取最优参数并构建了基于BP神经网络的水温预测模型;最后采用Java语言开发了基于B/S体系结构的预测系统。该系统在江苏省宜兴市河蟹养殖池塘进行了预测验证。结果表明:该系统在短期的水温预测中具有准确的预测效果,与传统的BP神经网络算法相比,研究内容评价指标平均绝对误差(MAE)、平均绝对百分误差(MAPE)和误差均方根(MSE)分别为0.196 8、0.007 9和0.059 2,均优于单一BP神经网络预测,可满足实际的养殖池塘水温管理需要。
[Abstract]:In order to solve the problems of low prediction accuracy and poor robustness of traditional non-real-time prediction method for small sample water temperature, a short-term prediction method of pond culture water temperature based on real-time data of Internet of things (IoT) was proposed based on genetic algorithm (GA) optimized BP neural network. On this basis, the prediction system of pond culture water temperature is designed and developed. Firstly, the key factors affecting pond water temperature are screened by principal component analysis, and the input elements are reduced, and then the initial weight and threshold are optimized by genetic algorithm. The optimal parameters are obtained and the water temperature prediction model based on BP neural network is constructed. Finally, a prediction system based on B / S architecture is developed by using Java language. The system was predicted and verified in river crab culture pond of Yixing City, Jiangsu Province. The results show that the system has accurate prediction effect in short-term water temperature prediction, compared with the traditional BP neural network algorithm. The average absolute error (MAE),) and mean absolute error (MAPE) and root mean square (RMS) (MSE) of the evaluation index were 0.196 and 0.059 2, respectively, which were superior to the prediction of single BP neural network, and could meet the requirement of water temperature management in culture pond.
【作者单位】： 中国农业大学信息与电气工程学院;农业部农业信息获取技术重点实验室;北京农业物联网工程技术研究中心;
【基金】：山东省重点研发计划项目(2015GGX101041) 上海市科技兴农重点攻关项目(沪农科攻字(2014)第4-6-2号) 广东省海大集团基于物联网技术的智慧水产养殖系统院士工作站(2012B090500008)
【分类号】：S964.3;TP183
本文编号：2250336
[Abstract]:In order to solve the problems of low prediction accuracy and poor robustness of traditional non-real-time prediction method for small sample water temperature, a short-term prediction method of pond culture water temperature based on real-time data of Internet of things (IoT) was proposed based on genetic algorithm (GA) optimized BP neural network. On this basis, the prediction system of pond culture water temperature is designed and developed. Firstly, the key factors affecting pond water temperature are screened by principal component analysis, and the input elements are reduced, and then the initial weight and threshold are optimized by genetic algorithm. The optimal parameters are obtained and the water temperature prediction model based on BP neural network is constructed. Finally, a prediction system based on B / S architecture is developed by using Java language. The system was predicted and verified in river crab culture pond of Yixing City, Jiangsu Province. The results show that the system has accurate prediction effect in short-term water temperature prediction, compared with the traditional BP neural network algorithm. The average absolute error (MAE),) and mean absolute error (MAPE) and root mean square (RMS) (MSE) of the evaluation index were 0.196 and 0.059 2, respectively, which were superior to the prediction of single BP neural network, and could meet the requirement of water temperature management in culture pond.
【作者单位】： 中国农业大学信息与电气工程学院;农业部农业信息获取技术重点实验室;北京农业物联网工程技术研究中心;
【基金】：山东省重点研发计划项目(2015GGX101041) 上海市科技兴农重点攻关项目(沪农科攻字(2014)第4-6-2号) 广东省海大集团基于物联网技术的智慧水产养殖系统院士工作站(2012B090500008)
【分类号】：S964.3;TP183
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1 杨争光;养殖水质数据处理与预测技术研究[D];太原科技大学;2015年
2 潘金晶;基于RBF神经网络的溶解氧预测模型研究[D];上海海洋大学;2016年
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