一种基于CEEMDAN-CPELM的池塘溶解氧预测模型研究  

Prediction Model for Dissolved Oxygen in Aquaculture Based on CEEMDAN-CPELM

作　　者：施珮 匡亮[3] 王泉 钱承山 袁永明 SHI Pei;KUANG Liang;WANG Quan;QIAN Chengshan;YUAN Yongming(School of IoT Engineering,Wuxi University,Wuxi Jiangsu 214105;Jiangsu Internet of Things Device Super Convergence and Security Research Center,Wuxi Jiangsu 214105;School of IoT Engineering,Jiangsu Vocational College of Information Technology,Wuxi Jiangsu 214153,China;Freshwater Fisheries Research Center,Chinese Academy of Fishery Sciences,Wuxi Jiangsu 214081,China)

机构地区：[1]无锡学院物联网工程学院,江苏无锡214105 [2]江苏省物联网设备超融合与安全工程研究中心,江苏无锡214105 [3]江苏信息职业技术学院物联网工程学院,江苏无锡214153 [4]中国水产科学研究院淡水渔业研究中心,江苏无锡214081

出　　处：《传感技术学报》2024年第1期89-97,共9页Chinese Journal of Sensors and Actuators

基　　金：江苏省高校自然科学研究面上项目(21KJB520020);无锡市“太湖之光”科技攻关项目(K20221044);国家自然科学基金项目(62072216);南京信息工程大学滨江学院人才启动经费项目(2021r038);江苏省教育科学“十四五”规划2021年度课题项目(B/2021/01/15);江苏信息职业技术学院科研重点课题项目(JSITKY202204);江苏高校“青蓝工程”资助项目。

摘　　要：针对集约化水产养殖中水体溶解氧时间序列非线性、非稳定性特征导致的溶解氧预测模型构建难度大、预测精度不高的问题,提出一种基于自适应完备集合经验模态分解-聚类重构结构的偏最小二乘优化极限学习机溶解氧预测模型(CEEMDAN-CPELM)。采用CEEMDAN方法将溶解氧数据流分解为不同频率的模态分量,并依据各分量的模糊熵值评估各分量的复杂度,利用K-medoids方法将所有分量按照模糊熵复杂度进行聚类,实现数据的分解-重构过程,降低溶解氧预测的难度;再利用偏最小二乘算法对极限学习机进行优化,提高模型的预测性能。最后,将CEEMDAN-CPELM模型应用到常熟养殖试验基地的水体溶解氧预测中。试验结果表明:基于CEEMDAN-CPELM的溶解氧预测模型的预测均方根误差值为0.959,明显低于GA-SELM、LSSVM和ELM等对比模型,验证了该预测模型的可行性和有效性。Aiming at the problem of high difficulty and low prediction accuracy during dissolved oxygen prediction modeling due to the nonlinear and unstable characteristics of dissolved oxygen time series in intensive aquaculture,a complete ensemble empirical mode de-composition with adaptive noise-clustering reconstitution and partial least squares optimized extreme learning machine model(CEEM-DAN-CPELM)is proposed.The CEEMDAN method is used to decompose the dissolved oxygen data stream into mode functions of differ-ent frequencies,and the complexity of each component is evaluated based on its fuzzy entropy value.The K-medoids method is used to cluster all functions according to their fuzzy entropy complexity,achieving the data decomposition reconstruction process and reducing the difficulty of dissolved oxygen prediction.Then,partial least squares algorithm is used to optimize the extreme learning machine and improve the predictive performance of the prediction model.Finally,the CEEMDAN-CPELM model is applied in aquaculture production at Changshu aquaculture experimental base.The experimental results show that the root mean square error value of the proposed CEEM-DAN-CPELM model is 0.959,which is significantly lower than the comparison models such as GA-SELM,LSSVM,and ELM,verifying the feasibility and effectiveness of the prediction model.

关 键 词：传感网络 溶解氧 预测 自适应完备集合经验模态分解 偏最小二乘法 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] TP212[自动化与计算机技术—计算机科学与技术] TP274.2