基于自组织模糊神经网络的大功率LED调光模型  

Dimming model of high-power LED based on self-organizing fuzzy neural network

作　　者：李纪宾 饶欢乐 王晨 钱依凡 洪哲扬 Li Jibin;Rao Huanle;Wang Chen;Qian Yifan;Hong Zheyang(School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区：[1]杭州电子科技大学自动化学院,浙江杭州310018

出　　处：《电子技术应用》2021年第12期105-109,共5页Application of Electronic Technique

基　　金：国家重大科研仪器研制基金项目(61427808);浙江省基础公益研究计划项目(LGG18F050002)。

摘　　要：大功率LED光度输出不仅与操作电流大小有关,且受传热过程的时滞时变不确定因素影响难以预测。针对传统机理建模存在参数提取困难、模型适应性弱等缺点,提出基于模糊神经网络建模算法,从而构建以操作电流、热沉温度、环境温度为输入,光通量为输出的调光模型。模型结构和参数依据在线数据进行调整,通过递推学习,模糊规则得到增量式完善,进而不断逼近实际动态过程。结果表明,利用该方法构建的调光模型与参考模型理论值相对误差小于3%,与其他模型相比,结构更加紧凑,预测精度更高。The luminosity output of high-power LED system is not only related to the current,but also hard to be predicted due to the uncertain nonlinear characters of thermal process.In view of the difficulties in extracting the parameters of the mechanism model and poor adaptability,an online modeling method was proposed to construct a fuzzy neural network with ambient temperature,heat sink temperature and operating current as input,and luminous flux as output.The model structure is self-organized and adjusted according to clustering analysis and error evaluation criteria.EKF algorithm and recursive least square method are used to learn network parameters.Through recursive learning,the rule is improved incrementally so that the model can approximate the actual system process as fast as possible.Validity of the algorithm is verified in a typical nonlinear system.Results show that the relative error between the theoretical values of the photometric prediction model and the reference model is less than 3%.Comparing with other model,this model has more compact structure and better generalization performance.

关 键 词：大功率LED 光电热模型 自组织模糊神经网络 结构辨识 参数学习 

分 类 号：TN364.2[电子电信—物理电子学]