Surface Quality Evaluation of Fluff Fabric Based on Particle Swarm Optimization Back Propagation Neural Network - MA Qiurui; LIN Qiangqiang; JIN Shoufeng (东华大学学报（英文版）, 2019)
TN911.73; Aiming at the problem that back propagation ( BP) neural network predicts the low accuracy rate of fluff fabric after fluffing process, a BP neural network model optimized by particle swarm optimization ( PSO) algorithm is proposed.The sliced image is obtained by the principle of light-cutting imaging.The fluffy region of the adaptive image segmentation is extracted by the Freeman chain code principle.The upper edge coordinate information of the fabric is subjected to one-dimensional discrete wavelet decomposition to obtain high frequency information and low frequency information.After comparison and analysis, the BP neural network was trained by high frequency information, and the PSO algorithm was used to optimize the BP neural network.The optimized BP neural network has better weights and thresholds.The experimental results show that the accuracy of the optimized BP neural network after applying high-frequency information training is 97.96％, which is 3.79％higher than that of the unoptimized BP neural network, and has higher detection accuracy.