计算机与现代化
計算機與現代化
계산궤여현대화
COMPUTER AND MODERNIZATION
2015年
6期
32-36
,共5页
溶解氧%水质预测%BP神经网络%思维进化算法
溶解氧%水質預測%BP神經網絡%思維進化算法
용해양%수질예측%BP신경망락%사유진화산법
dissolved oxygen%water quality prediction%BP neural network%Mind Evolutionary Computation
溶解氧作为水产养殖中最为重要且最容易控制的水质参数,其关系到养殖的成败,如果能精确掌握溶解氧的变化规律,可大大降低养殖风险,增加养殖成功率。本文综合考虑相关水质参数,建立BP神经网络水质预测模型,并在此基础上,构建基于思维进化算法( Mind Evolutionary Computation,MEC)的BP神经网络水质预测模型,通过对广西茅尾海海域的水质历史数据进行仿真实验,结果表明,思维进化BP神经网络预测值的精确度和准确度要高于BP神经网络。因此,将该算法应用于水产养殖水质预测是可行的。
溶解氧作為水產養殖中最為重要且最容易控製的水質參數,其關繫到養殖的成敗,如果能精確掌握溶解氧的變化規律,可大大降低養殖風險,增加養殖成功率。本文綜閤攷慮相關水質參數,建立BP神經網絡水質預測模型,併在此基礎上,構建基于思維進化算法( Mind Evolutionary Computation,MEC)的BP神經網絡水質預測模型,通過對廣西茅尾海海域的水質歷史數據進行倣真實驗,結果錶明,思維進化BP神經網絡預測值的精確度和準確度要高于BP神經網絡。因此,將該算法應用于水產養殖水質預測是可行的。
용해양작위수산양식중최위중요차최용역공제적수질삼수,기관계도양식적성패,여과능정학장악용해양적변화규률,가대대강저양식풍험,증가양식성공솔。본문종합고필상관수질삼수,건립BP신경망락수질예측모형,병재차기출상,구건기우사유진화산법( Mind Evolutionary Computation,MEC)적BP신경망락수질예측모형,통과대엄서모미해해역적수질역사수거진행방진실험,결과표명,사유진화BP신경망락예측치적정학도화준학도요고우BP신경망락。인차,장해산법응용우수산양식수질예측시가행적。
Dissolved oxygen as the most important and the most easily control water quality parameter of aquaculture relates to the success or failure of aquaculture. If we can accurately grasp the change rule of dissolved oxygen, the risk of breeding will greatly reduce and the breeding success rate will increase. In this paper, considering the related water quality parameters, we established the BP neural network model of water quality prediction. And on this basis, the BP neural network model based on mind evolu-tionary algorithm ( MEC) of water quality prediction was established. Through the simulation of historical data of water quality of Maowei Sea, Guangxi, the results show that the precision and accuracy of predicted value of MEC-BP neural network is higher than that of the BP neural network. Therefore, it is feasible to apply this algorithm to predict water quality of aquaculture.
