补偿模糊神经网络水果形状分级器分级误差  

Error of the compensation fuzzy neural network fruit grader

作　　者：曹乐平[1] 温芝元[2] 

机构地区：[1]湖南生物机电职业技术学院,长沙410127 [2]湖南农业大学理学院,长沙410128

出　　处：《农业工程学报》2008年第12期102-106,共5页Transactions of the Chinese Society of Agricultural Engineering

基　　金：湖南省教育厅科学研究项目(06D059)

摘　　要：针对神经网络对水果进行分级时精度有待提高的问题,分析了补偿模糊神经网络椪柑形状分级器的分级误差。将椪柑图像前4个傅里叶描述子按期望输出模糊变量值大小排列成单调递增、单调递减、钟形分布和锯齿形分布4种训练样本,分别训练同一补偿模糊神经网络水果形状分级器,用递减排序后的同一测试样本检验分级器性能,试验表明,单调递减顺序训练样本所训练的分级器分级误差最小为1.875%,钟形分布、单调递增顺序和锯齿形分布训练样本所训练的分级器分级误差依次增大,分别为15%、63.125%、75%。分析分级误差与样本间顺序的对应关系,建立分级误差模型,结果表明,同顺序的测试样本与训练样本间相关系数大,分级误差小;不同顺序的测试样本与训练样本间相关系数小,分级误差大。因此,测试样本与训练样本按水果同一品质特征同序排列,提高样本间的相关程度,将大幅度降低神经网络类分级器分级误差,提高正确识别率。This investigation analyses grading errors based on compensating fuzzy neural network to find out methods to improve citrus fruit grading.The first four harmonic components of discrete Fourier transform of citrus fruit images were sorted according to expected fuzzy values,and formed into four training sample sets which were in the order of monotone increasing,monotone decreasing,bell-shaped distribution and sawtooth distribution respectively.The new four sample sets were used to train compensating fuzzy neural network of the same architecture.Test set was sorted in monotone decreasing order.Test results show that model trained with monotone decreasing sample set has the smallest grading error which is 1.875%.Models trained with bell-shaped distribution,monotone increasing and sawtooth distribution have larger grading errors,which are 15%,63.125%,75%respectively.Grading error is modeled,and analyzed on correlation of grading error and differential order of sample sets.Results show that grading error is smaller with big correlation coefficient if training and testing sets are in the same order.On the contrary,if training and testing sample sets are in different orders,the correlation coefficient is smaller and the grading error is larger.This discloses testing and training sets shall be sorted in the same order according to qualitative feature as far as possible to improve the correlation coefficients.Performance of auto-grading system based on neural network model can be improved greatly by sorting samples in the same order.

关 键 词：补偿模糊神经网络 离散傅里叶变换 谐波分量 水果形状 分级误差 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] S66[自动化与计算机技术—控制科学与工程]