面向农业温室环境的ICDO-RBFNN多传感器数据融合算法  

ICDO-RBFNN multi-sensor data fusion for agricultural greenhouse environment

作　　者：罗焕芝 王骥 LUO Huanzhi;WANG Ji(School of Electronics and Information Engineering,Guangdong Ocean University,Zhanjiang 524088,China;Guangdong Engineering Technology Research Center of Intelligent Ocean Sensor Network and Equipment,Zhanjiang 524088,China)

机构地区：[1]广东海洋大学电子与信息工程学院,湛江524088 [2]广东省智慧海洋传感网及其装备工程技术研究中心,湛江524088

出　　处：《农业工程学报》2024年第21期184-191,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：广东省普通高校重点领域新一代信息技术专项(2020ZDZX3008)。

摘　　要：为改善农业环境传感器测量数据精度低、可靠性差的问题,该研究提出一种改进的切诺贝利灾难优化器(improved Chernobyl disaster optimizer,ICDO)优化径向基函数神经网络(radial basis function neural network,RBFNN)多传感器数据融合算法。首先引入佳点集、拉普拉斯交叉算子和修改位置更新方程改进切诺贝利灾难优化器(Chernobyl disaster optimizer,CDO),增强算法的寻优能力;再利用ICDO优化RBFNN模型,提升模型的稳定性;最后通过RBFNN模型的非线性映射能力实现多传感器数据融合方法,提高数据融合精度。仿真试验结果表明,大气环境质量预测的拟合优度达到0.999,均方误差低至0.348,平均绝对百分比误差降到0.729%;现场试验结果表明,温室环境等级划分的准确率高达99.21%,精准率为99.91%。研究提出的多传感器数据融合算法精度高,相对误差低,稳健性好。Agricultural sensors can greatly contribute to future technologies and systemic innovation in smart agriculture.However,the types and precision of sensors are limited to monitoring the agricultural environment with complex and diverse objects.The large and redundant monitoring data has also resulted in the low reliability of information perception.In this study,an improved radial basis function neural network(RBFNN)and Chernobyl disaster optimizer(ICDO)multi-sensor data fusion was proposed to improve the accuracy and reliability of single-sensor measurement.Firstly,an improved Chernobyl catastrophe optimization was performed on the neural network model.The good-point set theory was introduced to improve the initial population quality of the CDO,particularly for accuracy and speed.The adaptive Laplacian crossover operator was added to enhance the search performance.The better adaptive behavior was achieved in the high convergence speed.And then,the individual learning and differential evolution strategy were used to redefine the location update equation,in order to balance the local and global exploration.Secondly,the RBF neural network model was optimized by ICDO,in order to improve the stability of the model.Finally,the nonlinear mapping of the RBF neural network model was used to realize the multi-sensor data fusion with high accuracy.Three experiments were conducted to verify the improved model.The first one was to verify the ICDO.A large improvement was obtained in the solution accuracy and optimization stability,compared with particle swarm optimization(PSO),gray wolf optimization(GWO),firefly algorithm(FA),dung beetle optimizer(DBO),and subtraction average-based optimizer(SABO).The second one was to evaluate the quality of the atmospheric environment.Specifically,the atmospheric data was collected outside the South Subtropical Botanical Garden in Mazhang District,Zhanjiang City,Guangdong Province,China,from September 1,2022,to September 30,2023.The goodness of fit reached 0.999 for the prediction of atmosph

关 键 词：温室 多传感器 数据融合 ICDO RBF神经网络 

分 类 号：S625.51[农业科学—园艺学] TP183[自动化与计算机技术—控制理论与控制工程]