第2种方法，借助于BP神经网络的非线性映射能力与自适应能力来模拟纱线实

The second method uses BP neural network's non-linear mapping ability and self-adaptive ability to simulate the complex non-linear relationship between each input factor and output factor in the actual production of yarn, using key process factors and raw cotton quality factors as input variables , Separately designed the prediction method of "yarn quality" based on three-layer BP neural network and the prediction method of "yarn quality" based on four-layer BP neural network, and compared and analyzed the prediction effects of the two prediction methods.

The second method, with the help of the nonlinear mapping ability and adaptive ability of BP neural network to simulate the complex nonlinear relationship between the input factors and output factors in the actual production of yarn, takes the key process factors and raw cotton quality factors as input variables, designs the "yarn quality" prediction method based on the three-layer BP neural network and the "yarn mass" prediction method based on the four-layer BP neural network, and compares and analyzes the prediction effect of the two prediction methods.

The second method is to simulate the complex nonlinear relationship between the input factors and output factors in the actual production of yarn with the help of the nonlinear mapping ability and adaptive ability of BP neural network. Taking the key process factors and raw cotton quality factors as input variables, the "yarn quality" prediction method based on three-layer BP neural network and the "yarn quality" prediction method based on four-layer BP neural network are designed respectively The prediction method of "quality" and the prediction effect of the two methods are compared and analyzed.