中圖分類號(hào)： TN364+.2
文獻(xiàn)標(biāo)識(shí)碼： A
DOI：10.16157/j.issn.0258-7998.201125
中文引用格式： 李紀(jì)賓,，饒歡樂(lè),，王晨，等. 基于自組織模糊神經(jīng)網(wǎng)絡(luò)的大功率LED調(diào)光模型[J].電子技術(shù)應(yīng)用,，2021,，47(12)：105-109.
英文引用格式： Li Jibin，Rao Huanle，Wang Chen,，et al. Dimming model of high-power LED based on self-organizing fuzzy neural network[J]. Application of Electronic Technique,，2021，47(12)：105-109.

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

Abstract： 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.

Key words : high-power LED；PET model,；self-organizing fuzzy neural network,；structure identification；parameter learning