In this paper,a hybrid model based on sooty tern optimization algo-rithm(STOA)is proposed to optimize the parameters of the support vector machine(SVM)and identify the best feature sets simultaneously.Feature selec-ti...In this paper,a hybrid model based on sooty tern optimization algo-rithm(STOA)is proposed to optimize the parameters of the support vector machine(SVM)and identify the best feature sets simultaneously.Feature selec-tion is an essential process of data preprocessing,and it aims to find the most rele-vant subset of features.In recent years,it has been applied in many practical domains of intelligent systems.The application of SVM in many fields has proved its effectiveness in classification tasks of various types.Its performance is mainly determined by the kernel type and its parameters.One of the most challenging process in machine learning is feature selection,intending to select effective and representative features.The main disadvantages of feature selection processes included in classical optimization algorithm are local optimal stagnation and slow convergence.Therefore,the hybrid model proposed in this paper merges the STOA and differential evolution(DE)to improve the search efficiency and con-vergence rate.A series of experiments are conducted on 12 datasets from the UCI repository to comprehensively and objectively evaluate the performance of the proposed method.The superiority of the proposed method is illustrated from dif-ferent aspects,such as the classification accuracy,convergence performance,reduced feature dimensionality,standard deviation(STD),and computation time.展开更多
针对小白菜生长中存在的养分供应不平衡与施肥量预测模型精度不足的问题,该研究搭建了一套基于实时土壤养分检测的变量施肥机,可在单次作业中实现土壤参数的在线采集、施肥需求预测与施肥执行的协同运行。同时运用机器学习方法,结合小...针对小白菜生长中存在的养分供应不平衡与施肥量预测模型精度不足的问题,该研究搭建了一套基于实时土壤养分检测的变量施肥机,可在单次作业中实现土壤参数的在线采集、施肥需求预测与施肥执行的协同运行。同时运用机器学习方法,结合小白菜历史生长环境与养分数据,构建了基于混合乌燕鸥算法优化的BP神经网络(backpropagation neural network model based on hybrid sooty tern optimization algorithm,HA-STOA-BP)预测模型。预测结果与BP神经网络预测模型、基于鲸鱼算法优化的BP神经网络预测模型(WOA-BP)以及基于乌燕鸥算法优化的BP神经网络(STOA-BP)预测模型进行比较,结果显示HA-STOA-BP模型预测值与实际施肥量的变化趋势高度一致,模型平均决定系数达0.970,而STOA-BP模型、WOA-BP模型以及BP模型平均决定系数分别为0.867、0.815以及0.656;同时HA-STOA-BP预测模型的最大绝对百分比误差为9.89%,均小于STOA-BP模型、WOA-BP模型以及BP模型最大绝对百分比误差的17.17%、18.15%、24.19%,表明该预测模型具有更好的预测性能。在此基础上,通过田间试验对变量施肥装置在不同作业速度下的排肥稳定性与作业性能进行了系统评估。选取0.30、0.65和0.80 m/s三种典型作业速度开展排肥精度测试。试验结果表明,在0.30 m/s作业速度下,平均排肥精度达到97.5%;在0.65 m/s作业速度下,平均排肥精度为95.1%。随着作业速度的提高,排肥精度出现一定程度的下降趋势,但在0.80 m/s条件下平均排肥精度仍保持在91.0%。上述结果表明,所提出的变量施肥机排肥策略模型能够提高小白菜施肥量预测的精度,可为实现快速、精准和高效的变量施肥提供参考。展开更多
基金Sanming University introduces high-level talents to start scientific research funding support project(20YG14,20YG01)Guiding science and technology projects in Sanming City(2020-G-61,2020-S-39)+1 种基金Educational research projects of young and middle-aged teachers in Fujian Province(JAT200618,JAT200638)Scientific research and development fund of Sanming University(B202009,B202029).
文摘In this paper,a hybrid model based on sooty tern optimization algo-rithm(STOA)is proposed to optimize the parameters of the support vector machine(SVM)and identify the best feature sets simultaneously.Feature selec-tion is an essential process of data preprocessing,and it aims to find the most rele-vant subset of features.In recent years,it has been applied in many practical domains of intelligent systems.The application of SVM in many fields has proved its effectiveness in classification tasks of various types.Its performance is mainly determined by the kernel type and its parameters.One of the most challenging process in machine learning is feature selection,intending to select effective and representative features.The main disadvantages of feature selection processes included in classical optimization algorithm are local optimal stagnation and slow convergence.Therefore,the hybrid model proposed in this paper merges the STOA and differential evolution(DE)to improve the search efficiency and con-vergence rate.A series of experiments are conducted on 12 datasets from the UCI repository to comprehensively and objectively evaluate the performance of the proposed method.The superiority of the proposed method is illustrated from dif-ferent aspects,such as the classification accuracy,convergence performance,reduced feature dimensionality,standard deviation(STD),and computation time.
文摘针对小白菜生长中存在的养分供应不平衡与施肥量预测模型精度不足的问题,该研究搭建了一套基于实时土壤养分检测的变量施肥机,可在单次作业中实现土壤参数的在线采集、施肥需求预测与施肥执行的协同运行。同时运用机器学习方法,结合小白菜历史生长环境与养分数据,构建了基于混合乌燕鸥算法优化的BP神经网络(backpropagation neural network model based on hybrid sooty tern optimization algorithm,HA-STOA-BP)预测模型。预测结果与BP神经网络预测模型、基于鲸鱼算法优化的BP神经网络预测模型(WOA-BP)以及基于乌燕鸥算法优化的BP神经网络(STOA-BP)预测模型进行比较,结果显示HA-STOA-BP模型预测值与实际施肥量的变化趋势高度一致,模型平均决定系数达0.970,而STOA-BP模型、WOA-BP模型以及BP模型平均决定系数分别为0.867、0.815以及0.656;同时HA-STOA-BP预测模型的最大绝对百分比误差为9.89%,均小于STOA-BP模型、WOA-BP模型以及BP模型最大绝对百分比误差的17.17%、18.15%、24.19%,表明该预测模型具有更好的预测性能。在此基础上,通过田间试验对变量施肥装置在不同作业速度下的排肥稳定性与作业性能进行了系统评估。选取0.30、0.65和0.80 m/s三种典型作业速度开展排肥精度测试。试验结果表明,在0.30 m/s作业速度下,平均排肥精度达到97.5%;在0.65 m/s作业速度下,平均排肥精度为95.1%。随着作业速度的提高,排肥精度出现一定程度的下降趋势,但在0.80 m/s条件下平均排肥精度仍保持在91.0%。上述结果表明,所提出的变量施肥机排肥策略模型能够提高小白菜施肥量预测的精度,可为实现快速、精准和高效的变量施肥提供参考。
