辛萍萍,张珍,王智永,胡瑾,邵志成,张海辉.基于支持向量机-改进型鱼群算法的CO2优化调控模型[J].农业机械学报,2017,48(6):249-256.
XIN Pingping,ZHANG Zhen,WANG Zhiyong,HU Jin,SHAO Zhicheng,ZHANG Haihui.Carbon Dioxide Optimal Control Model Based on Support Vector-Improved Fish Swarm Algorithm[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(6):249-256.
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基于支持向量机-改进型鱼群算法的CO2优化调控模型   [下载全文]
Carbon Dioxide Optimal Control Model Based on Support Vector-Improved Fish Swarm Algorithm   [Download Pdf][in English]
投稿时间:2016-10-09  
DOI:10.6041/j.issn.1000-1298.2017.06.032
中文关键词:  CO2优化调控模型  支持向量机算法  改进型鱼群算法  光合速率  CO2饱和点
基金项目:国家自然科学基金项目(31671587、31501224)和陕西省农业科技创新与攻关项目(2016NY-125)
作者单位
辛萍萍 西北农林科技大学机械与电子工程学院农业部农业物联网重点实验室 
张珍 西北农林科技大学机械与电子工程学院农业部农业物联网重点实验室 
王智永 西北农林科技大学机械与电子工程学院农业部农业物联网重点实验室 
胡瑾 西北农林科技大学机械与电子工程学院农业部农业物联网重点实验室 
邵志成 西北农林科技大学机械与电子工程学院农业部农业物联网重点实验室 
张海辉 西北农林科技大学机械与电子工程学院农业部农业物联网重点实验室 
中文摘要:提出了融合支持向量机-改进型鱼群算法的CO2优化调控模型,为CO2精准调控提供定量依据。设计了嵌套试验,采集不同温度、光子通量密度、CO2浓度组合下的黄瓜光合速率,以此构建基于支持向量机的黄瓜光合速率预测模型;以预测模型网络为目标函数,采用改进型鱼群算法实现二氧化碳饱和点寻优,获得不同温度、光子通量密度组合条件的CO2饱和点,进而构建CO2优化调控模型。异校验结果表明,CO2饱和点实测值与预测值相关系数为0.965,最大相对误差3.056%。提出的CO2优化调控模型可动态预测CO2饱和点,为实现设施CO2精准调控提供了可行思路。
XIN Pingping  ZHANG Zhen  WANG Zhiyong  HU Jin  SHAO Zhicheng  ZHANG Haihui
College of Mechanical and Electronic Engineering, Northwest A&F University;Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture,College of Mechanical and Electronic Engineering, Northwest A&F University;Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture,College of Mechanical and Electronic Engineering, Northwest A&F University;Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture,College of Mechanical and Electronic Engineering, Northwest A&F University;Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture,College of Mechanical and Electronic Engineering, Northwest A&F University;Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and College of Mechanical and Electronic Engineering, Northwest A&F University;Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture
Key Words:CO2 optimal regulation model  support vector machine algorithm  improved fish swarm algorithm  photosynthetic rate  saturation point of CO2
Abstract:CO2 was one of the main raw materials for plant photosynthetic rate, CO2 optimal regulation model to meet the crops’ requirements was pivotal to afford a fine growth environment in crops’ whole life cycle. CO2 optimal regulation model fusing the support vector machine-improved fish swarm algorithm was proposed to provide a quantitative basis for precise regulation of CO2 in greenhouse. Taking the cucumber plant as research object, considering the mechanism of its photosynthesis, a photosynthesis rate nest test with three factor combinations consisted of temperature, photon flux density and CO2 concentration was constructed. In the test, temperatures, photon flux densities and CO2 concentrations were set at 9, 7, 10 gradients, respectively. Totally 630 groups of CO2 response data were obtained by LI-6400XT portable photosynthesis rate instrument, in which 81% of the data was employed to construct the support vector machine (SVM) photosynthetic rate prediction model, while the remaining data was used for model validation. Furthermore, through improved fish swarm algorithm with SVM photosynthetic rate prediction model network as input, optimized photosynthetic rate values were acquired with variety of variables. Accordingly, CO2 saturation points were generated at different temperatures and photon flux density conditions for CO2 optimal regulation model. Compared the proposed SVM photosynthetic rate prediction model with conventional non-linear regression (NLR) prediction model and error back propagation (BP) prediction model, results showed that SVM prediction model was obviously superior to NLR prediction model and BP prediction model with correlation coefficient of 0.994 and mean absolute error of 0.879μmol/(m2·s). Then, XOR checkout was adopted to validate the CO2 optimal regulation model, results showed that the correlation coefficient between the simulated values and measured values was 0.965 and the maximum relative error was 3.056%, which indicated that the proposed CO2 optimization model could be applied to predict CO2 saturation points dynamically and provide a feasible way for CO2 concentration precise controlling for plants in greenhouse.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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