农业工程学报
農業工程學報
농업공정학보
2014年
3期
155-162
,共8页
刘双印%徐龙琴%李道亮%曾立华
劉雙印%徐龍琴%李道亮%曾立華
류쌍인%서룡금%리도량%증립화
水产养殖%水质%模型%支持向量机%在线预测%特征点分段时间弯曲距离%相似数据
水產養殖%水質%模型%支持嚮量機%在線預測%特徵點分段時間彎麯距離%相似數據
수산양식%수질%모형%지지향량궤%재선예측%특정점분단시간만곡거리%상사수거
aquaculture%water quality%models%support vector machine%online prediction%feature points segmented time warping distance%similar data
为及时辨识集约化水产养殖水质变化趋势、动态调控水质，确保无应激环境下健康养殖，该文提出了基于时序列相似数据的最小二乘支持向量回归机（least squares support vector regression，LSSVR）水质溶解氧在线预测模型。采用特征点分段时间弯曲距离（feature points segmented time warping distance，FPSTWD）算法对在线采集的时间序列数据进行分段与相似度计算，以缩减规模的子序列数据集对 LSSVR 模型进行快速训练优化，实现了多个LSSVR子模型在线建模，将预测数据序列与LSSVR子模型的相似度匹配，自适应地选取最佳的子模型作为在线预测模型。应用该模型对集约化河蟹福利养殖水质参数溶解氧浓度进行在线预测，模型评价指标中最大相对误差、平均绝对百分比误差、相对均方根误差和运行时间分别为4.76%、8.18%、5.23%、8.32 s。研究结果表明，与其他预测方法相比，该模型具有较好的综合预测性能，能够满足河蟹福利养殖水质在线预测的实际需求，并为集约化水产养殖水质精准调控提供研究基础。
為及時辨識集約化水產養殖水質變化趨勢、動態調控水質，確保無應激環境下健康養殖，該文提齣瞭基于時序列相似數據的最小二乘支持嚮量迴歸機（least squares support vector regression，LSSVR）水質溶解氧在線預測模型。採用特徵點分段時間彎麯距離（feature points segmented time warping distance，FPSTWD）算法對在線採集的時間序列數據進行分段與相似度計算，以縮減規模的子序列數據集對 LSSVR 模型進行快速訓練優化，實現瞭多箇LSSVR子模型在線建模，將預測數據序列與LSSVR子模型的相似度匹配，自適應地選取最佳的子模型作為在線預測模型。應用該模型對集約化河蟹福利養殖水質參數溶解氧濃度進行在線預測，模型評價指標中最大相對誤差、平均絕對百分比誤差、相對均方根誤差和運行時間分彆為4.76%、8.18%、5.23%、8.32 s。研究結果錶明，與其他預測方法相比，該模型具有較好的綜閤預測性能，能夠滿足河蟹福利養殖水質在線預測的實際需求，併為集約化水產養殖水質精準調控提供研究基礎。
위급시변식집약화수산양식수질변화추세、동태조공수질，학보무응격배경하건강양식，해문제출료기우시서렬상사수거적최소이승지지향량회귀궤（least squares support vector regression，LSSVR）수질용해양재선예측모형。채용특정점분단시간만곡거리（feature points segmented time warping distance，FPSTWD）산법대재선채집적시간서렬수거진행분단여상사도계산，이축감규모적자서렬수거집대 LSSVR 모형진행쾌속훈련우화，실현료다개LSSVR자모형재선건모，장예측수거서렬여LSSVR자모형적상사도필배，자괄응지선취최가적자모형작위재선예측모형。응용해모형대집약화하해복리양식수질삼수용해양농도진행재선예측，모형평개지표중최대상대오차、평균절대백분비오차、상대균방근오차화운행시간분별위4.76%、8.18%、5.23%、8.32 s。연구결과표명，여기타예측방법상비，해모형구유교호적종합예측성능，능구만족하해복리양식수질재선예측적실제수구，병위집약화수산양식수질정준조공제공연구기출。
Water quality regulation is one of the most important tasks in intensive aquaculture management. Grasping the trend of the dissolved oxygen concentration timely and accurately and regulating water quality dynamics are the key for healthy growth in the non-stress environment of aquatic products in order to solve the low prediction accuracy, inferior capability of dynamic learning, online updates, and high computational complexity of the traditional online forecasting methods for water quality in intensive aquaculture. The online prediction model of dissolved oxygen content in intensive aquaculture eriocheir sinensis cultures was introduced, which was based on the least squares support vector machine (LSSVR) with time series similar data. The time series data collected online was segmented clustered using a feature points segmented time warping distance algorithm. The subsequence data sets reduced the size and optimized the LSSVR models training process, achieving multiple LSSVR models online modeling, and segmented memory and storage. According to the forecast data sequence and LSSVR sub-model similarity, it adaptively chose the optimal sub-model to get the predicted output. The online model was used for the prediction of the dissolved oxygen changing in high-density eriocheir sinensis culture ponds during July 21, 2012 to July 31, 2012 in Yixing City, Jiangsu Province, China. Experimental results showed that the proposed prediction model of FPSTWD-LSSVR had a better prediction effect than the FPSTWD-LSSVR, ILSSVR, SONB-LSSVR, or off-line LSSVR algorithms. Under the same experimental conditions, the relative mean absolute percentage error (MAPE), maximum relative error (Emax), relative root mean square error (RRMSE), and the running time differences between the FPSTWD-LSSVR and ILSSVR models were 47.93%, 43.47%%, 30.91%, and 5.16 s in the test period respectively. The relative MAPE, Emax, RRMSE, and the running time differences between the FPSTWD-LSSVR and SONB-LSSVR models were 39.99%, 33.43%, 22.40%, and 2.74 s in the test period respectively. It is obvious that FPSTWD-LSSVR is more accurate than ILSSVR and SONB-LSSVR. The relative MAPE,Emax, RRMSE and the running time differences between the FPSTWD-LSSVR and off-line LSSVR models were 16.14%, 9.03%, 8.41%, and 11.36 s in the test period respectively. The lower sample number, which cannot cover all types of characteristic in time series data, probably caused the prediction performance of FPSTWD-LSSVR to be slightly lower than the off-line LSSVR model. Overall, the online prediction model has a low computational complexity, fast convergence rate, high online prediction accuracy, and strong generalization ability. It is an effective online prediction method for the dissolved oxygen controlling in the high density eriocheir sinensis culture, and provides the basis of decisions for controlling water quality, setting the aquaculture water plan, and reducing the risk of cultivation.
