罗非鱼池塘养殖溶解氧预测研究

为及时有效地掌握池塘养殖中溶解氧浓度的变化趋势，保障罗非鱼稳定高效养殖，在对罗非鱼池塘养殖实际情况进行研究和分析的基础上，采用粒子群算法对BP神经网络模型进行参数优化，针对无锡市2015 年8 月23—11 月4 日这段时间内南泉实验基地的水产养殖溶解氧进行预测。同时，将粒子群优化BP神经网络模型与BP神经网络模型的训练结果和预测结果进行对比。研究结果表明，PSO-BP优化模型的训练和预测结果远远优于普通BP神经网络模型，除异常点外，误差率基本均低于0.5%。同时，该模型收敛速度快，计算复杂性低，能够较好的体现和预测罗非鱼池塘养殖的溶解氧趋势，也为其他水质指标的预测提供了研究方向。

Prediction of Dissolved Oxygen Concentration in Tilapia Aquaculture Pond

In order to timely and effectively know the tendency of the dissolved oxygen concentration in tilapia aquaculture pond and guarantee the stable and efficient tilapia aquaculture, based on the analysis of the practical pond environment, particle swarm optimization (PSO) algorithm was used to optimize the parameters of BP neural network model. Then the dissolved oxygen concentrations of the ponds in Nanquan experimental base in Wuxi from August 23rd to November 4th in 2015 were predicted. And the training and prediction results between optimized model PSO-BP and the common BP model were compared. The results showed that training and prediction results of PSO-BP model were much better than that of the common BP model, and the error rate of PSO-BP model was lower than 0.5% except the abnormal points. PSO-BP model has a fast convergence speed and low computing complexity. Besides, it can accurately predict dissolved oxygen concentration of tilapia aquaculture pond and provide a research direction for the prediction of other water quality indicators.