Raw cotton short fiber index prediction model based on BP neural network

In order to predict the short fiber index of raw cotton,a prediction model based on back propagation(BP) neural network combining genetic algorithm was designed.Based on the raw cotton in southern Xinjiang,the seed cotton moisture regain and the rotational speed frequency of saw cylinder were adopted as the basic characteristic quantity of BP neural networks models.Genetic algorithm was used to optimize the weights and bias of BP neural work.Optimized BP neural network was applied to predict the short fiber index of raw cotton.The results show that the optimized BP neural network model can effectively reflect the nonlinear relationship between the short fiber index of raw cotton and main controlling factors.The small errors between the prediction values and measured values were achieved.The R-square of regression function between output and target of neural networks model for testing sample is 0.999 8.