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李小昱,汪小芳,王为,张军.基于机械特性BP神经网络的苹果贮藏品质预测[J].农业工程学报,2007,23(5):150-153.DOI:
基于机械特性BP神经网络的苹果贮藏品质预测
投稿时间:2006-04-19  修订日期:2006-09-14
中文关键词:  苹果  BP神经网络  机械特性  贮藏品质  预测模型
基金项目:
作者单位
李小昱 教授博士生导师主要从事智能化检测与控制技术农业生物力学农产品加工与贮藏技术等方面的教学与科研。湖北省武汉市狮子山街特1号华中农业大学工程技术学院430070。Email:lixiaoyu@mail.hzau.edu.cn 
汪小芳 华中农业大学工程技术学院武汉 430070常州工程职业技术学院机械工程技术系常州 213164 
王为 华中农业大学工程技术学院武汉 430070 
张军 华中农业大学工程技术学院武汉 430070 
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中文摘要:应用L-M优化算法BP神经网络,通过用苹果机械特性指标(压缩时的最大力、屈服力、弹性模量)预测苹果贮藏品质(硬度、水分、可溶性固形物、总酸)的方法,建立贮藏品质的人工神经网络模型。用试验所测的机械特性指标为输入,苹果贮藏品质为输出来确定网络的拓扑结构,训练建立的BP神经网络。仿真结果表明:该神经网络模型用机械特性指标能预测苹果贮藏品质,同时通过5组非样本数据来验证该神经网络,模型的预测值与实测值的相对误差在5%以下,能够满足工程应用中预测苹果贮藏品质的精度要求。
Li Xiaoyu,Wang Xiaofang,Wang Wei,Zhang Jun.Estimation of apple storage quality properties based on the mechanical properties with BP neural network[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2007,23(5):150-153.DOI:
Estimation of apple storage quality properties based on the mechanical properties with BP neural network
Author NameAffiliation
Li Xiaoyu College of Engineering and Technology, Huazhong Agricultural University, Wuhan 430070, China 
Wang Xiaofang College of Engineering and Technology, Huazhong Agricultural University, Wuhan 430070, China
Department of mechanical engineering and technology, Changzhou Institute of Engineering Technology, Changzhou 213164, China 
Wang Wei College of Engineering and Technology, Huazhong Agricultural University, Wuhan 430070, China 
Zhang Jun College of Engineering and Technology, Huazhong Agricultural University, Wuhan 430070, China 
Key words:apple  BP neural network  mechanical property  quality property  estimation model
Abstract:Apple storage quality properties(including hardness, moisture, soluble solid, total acid) were estimated through the mechanical properties of apple(including the maximum of compression, the yield force, the elastic modulus). An artificial neural network model of storage quality properties was built by the optimization algorithm of L-M(levernberg marquardt) BP neural network. The mechanical properties and the apple storage quality properties measured in the experiment were adopted as input and output to establish the BP neural network. The simulated results show that this neural network make a good estimation of apple storage quality properties through mechanical properties. When tested by five groups of Non-sample data, the relative error between the estimation of this model and the measured value is below 5%, which meets the accuracy requirement of apple storage quality properties in engineering application.
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