基于改进型鱼群算法的番茄光环境调控目标值模型

发布时间：2018-05-15 01:17

  本文选题：番茄 + 改进型鱼群算法　； 参考：《农业机械学报》2016年01期

【摘要】：针对光温耦合条件下番茄光环境调控目标值难以快速、精确获取的问题,在光温嵌套光合速率试验结果基础上,为提高人工鱼群算法寻优速度,基于视野和步长动态调整思想,提出了改进型鱼群算法的光温耦合寻优方法,对不同温度下光饱和点进行快速精准寻优,建立了番茄光环境调控目标值模型,模型决定系数为0.999 9。验证试验结果表明,不同温度下光饱和点的模型计算值与实测值高度线性相关,相关系数为0.988,最大相对误差在±2%内,明显优于遗传算法构建模型的相对误差(±6%)。快速、动态获取不同温度下光饱和点,对设施光环境精准调控效率具有重要意义。
[Abstract]:In order to improve the optimization speed of artificial fish swarm algorithm, aiming at the problem that it is difficult to obtain the target value of tomato light environment regulation quickly and accurately under the condition of light and temperature coupling, based on the idea of dynamic adjustment of visual field and step size, based on the experiment results of light and temperature nesting photosynthetic rate, In this paper, an improved fish swarm algorithm is proposed, which can be used to optimize the light saturation point quickly and accurately at different temperatures. The target value model of tomato light environment regulation is established, and the determining coefficient of the model is 0.999 9. The experimental results show that the calculated values of the light saturation point at different temperatures are linearly correlated with the measured values, the correlation coefficient is 0.988, and the maximum relative error is within 卤2%, which is obviously superior to the relative error (卤6%) of the genetic algorithm in constructing the model. It is very important to obtain the saturation point of light at different temperature quickly and dynamically, which is very important to the precision control efficiency of facility light environment.
【作者单位】： 西北农林科技大学机械与电子工程学院;
【基金】：国家自然科学基金项目(31501224) “十二五”国家科技支撑计划项目(2012BAH29B04) 陕西省科学技术研究发展项目(2013K02-03、2014K08-02-03、2014K02-08-02)
【分类号】：S641.2;TP18
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本文编号：1890324