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出 处:《农业工程学报》2007年第5期150-153,共4页Transactions of the Chinese Society of Agricultural Engineering
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。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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