PSO-BP Neural Network-Based Gelatin Hollow Capsule Color Matching Model

To precisely formulate the color during the production of gelatin hollow capsules, a gelatin hollow capsule color matching model based on particle swarm optimization back propagation (PSO-BP) neural network is proposed. The experiment of gelatin color matching was carried out and images of the gelatin solution were collected. MATLAB image processing toolbox was utilized to conduct image filtering and image segmentation, improving the accuracy of color characteristics extracting. The BP neural network's initial parameters were optimized by the particle swarm optimization algorithm, building the relation model between the RGB values of two sets of gelatin solution and the difference between the added pigments. The experimental data was used to train and test the model. It can be concluded that the prediction effect of the model is satisfying, and the training speed and prediction accuracy have been improved to a certain extent compared with the model of traditional BP neural network.