基于支持向量机-改进型鱼群算法的CO2优化调控模型

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.%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 0.879 μmol/(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.