摘要
在分析了工厂化水产养殖池塘溶解氧影响因素的基础上,利用RBF神经网络良好的非线性逼近能力建立了池塘溶解氧的神经网络预测模型。常规的RBF神经网络模型常导致训练时间较长且易陷入局部极小点,因此,采用自适应遗传算法对RBF神经网络进行优化,模型的收敛速度明显加快。采用了养殖池塘的外部可控环境水体温度T、水流量Q、酸度(pH)以及增氧机器的转速V作为模型的输入。实验结果表明采用该方法预报溶解氧的预测精度较常规RBF递推算法的预测精度明显提高。该方法为研制开发智能水产养殖环境监控系统以及工厂化水产养殖奠定了基础。
In this paper the prediction model of dissolved is established by using nonlinear approximation ability of RBF neural networks,which is based on an analysis of infection factors of dissolved oxygen in aquaculture ponds,and adaptive genetic algorithm is introduced to optimize the RBF neural networks,because the conventional RBF neural network model often leads to longer training time and falls into local minimum easily.This paper applies the external environment factors controlled of aquaculture pond as a model input,which includes water temperature（T）,water flux（Q）,acidity（pH） and the oxygen machine speed（V）.Experimental results have shown that the prediction accuracy of the proposed method of dissolved oxygen is higher than the conventional recursive RBF algorithm,prediction accuracy is significantly improved.The method serves as the foundation for the monitoring system development of the intelligent aquaculture environment and factory aquaculture.
出处
《中国农村水利水电》
北大核心
2011年第2期14-16,22,共4页
China Rural Water and Hydropower
基金
江苏省工业攻关项目(BE2006090)
关键词
溶解氧
遗传算法
神经网络
预测模型
dissolved oxygen
genetic algorithm
neural network
forecast model
