渔业现代化Issue(1)：24-29,6.DOI:10.3969/j.issn.1007-9580.2016.01.005
基于粒子群优化 BP神经网络的水产养殖水温及 pH预测模型
Prediction model of aquaculture water temperature and pH based on BP neural network optimized by particle swarm algorithm
摘要
Abstract
Focused on the problem of inaccurate aquaculture water temperature and pH prediction , a mixed algorithm for water quality parameters prediction which was based on particle swarm optimization BP neural network ( PSO-BPNN) was proposed .Firstly, the particle swarm optimization ( PSO) algorithm was applied in calculating the initial weights and thresholds of BP neural network ( BPNN) .Secondly , the abnormal data were fixed andthe six parameters of water quality as inputs were used , the temperature and pH value of the next time point were used as outputs to establish aquaculture water quality prediction model .Finally, the collected water quality data were used to conduct training in BP neural network , and the feasibility and performance of water quality prediction model was tested through experiments .Compared with support vector regression ( SVR) and normal BP neural network , in the aspect of predicting water temperature using PSO-BPNN, the decreasing amplitudes of RMSE were 64%and 80%respectively , while in the aspect of predicting pH value , the decreasing amplitudes of RMSE were 32% and 65% respectively .The results of experiments show that aquaculture water quality prediction model based on PSO-BPNN is flexible, simple, convenient and it also has a good capacity of prediction .关键词
粒子群算法/BP神经网络/水产养殖/渔情预警/水质预测模型Key words
particle swarm optimization(PSO)/BP neural network(BPNN)/aquaculture/fishing condition warning/water quality prediction model分类
信息技术与安全科学引用本文复制引用
徐大明,周超,孙传恒,杜永贵..基于粒子群优化 BP神经网络的水产养殖水温及 pH预测模型[J].渔业现代化,2016,(1):24-29,6.基金项目
国家863计划项目（2012 AA101905-02）；北京市自然科学基金资助项目（6152009）；国家现代农业产业技术体系建设专项 （）
