Yarn Quality Prediction Based on Improved BP Neural Network

Aiming at the key quality indexes xbt in spinning processing is caused by many complex and interactions factors. A xbt prediction model is put forward based on the PSO-BP neural network, which adjusts weights of BP neural network using particle swarm optimization (PSO) rather than the traditional gradient descent method, is used to improve the convergence speed of neural network and the ability of getting the global optimal solution. As the object of a large number of field detection data in a spinning workshop, the results show that, compared with the traditional BP algorithm and GA-BP algorithm, the PSO-BP neural network can obvious improve yarn quality prediction model precision and stability.