基于BP神经网络的皮棉杂质在线检测方法

    On-line measurement of trash contents in cotton based on BP neural network

    • 摘要: 为改进因皮棉含杂影响因素多,现有杂质含量数学统计模型不精确,测量精度不高的缺点,用人工神经网络方法建立了基于BP神经网络的皮棉杂质含量数学模型。用自行设计的皮棉杂质测量系统提取皮棉杂质图像特征参数并进行处理。针对BP神经网络收敛慢的特点,在实际算法中引入了动量项,从而提高了网络收敛速度。试验结果表明,BP神经网络模型拟合结果的相对剩余标准差为1.76%,拟合精度明显优于数理统计模型的拟合精度。

       

      Abstract: To improve the existing trash content, in cotton statistical models, which are inaccurate and imprecise in measurement as there are many factors influencing trash content in cotton, a model based on the BP neural network was constructed. A measurement system was designed to pick up image feature parameters of trash in cotton and to dispose these parameters. As for the slow convergence rate of BP algorithm, a momentum item was introduced into BP algorithm so that the convergence rate was increased. Experimental results show that the relative residual standard deviation of the fitted value of the BP neural network is 1.76% and the accuracy of its fitted value is much higher than that of other statistical models.

       

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