Study on phosphor powder precipitation model in flexible material manufacturing process based on neuro-fuzzy network

The precipitation of LED phosphor glue is not only related to the physical properties of phosphor powder and silica gel, but also influenced by the uncertainties in the production process. In this paper, support vector clustering (SVC) is combined with T-S neuro-fuzzy network to build the neuro-fuzzy network prediction model of phosphor powder precipitation. The structure identification of the predictive model and the neuro-fuzzy network parameter learning algorithm are derived. Finally, the flow chart of the modeling of predictive model is given. The test results show that the training time of the new TSFNN proposed in this paper is 56% less than the standard TSFNN model and the average error of the new TSFNN is 33.33% less than the standard one. LED phosphor powder mixing system test shows that the new TSFNN model control system effectively enhances the LED light color consistency comparing with the traditional method.