Analysis and Prediction of Thermal Resistance for High Power LED Using GA-SVR

Abstract

With the development of high power LED technology, junction temperature as a key factor constrains the performance and the service life of LED, and the main parameter of junction temperature is thermal resistance. Therefore, how to measure the thermal resistance of high power LED quickly and accurately plays an important part in improving the performance and the service life of LED. In this paper the accurate and fast measurement equipment was applied to study the thermal characteristics of high power LED. The forward-voltage based method was conducted to measure the junction temperature of high power. Then, support vector regression (SVR) combined with genetic algorithm (GA) for its parameter optimization, was proposed to establish a model to predict the thermal resistance of high power LED. The prediction performance of GA-SVR was compared with those of BPNN model. The result demonstrated that the estimated errors GA-SVR models, such as Mean Absolute Relative Error (MARE) and Root Mean Squared Errors (RMSE), all are smaller than those achieved by the BPNN applying identical samples.