基于Adam算法和神经网络的照度计算方法
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摘要
针对传统照度计算中利用系数计算过程繁琐、误差大的问题,提出并实现了基于Adam优化算法的由固定网络和可变网络并联构成的神经网络模型,进行灯具的利用系数拟合计算,分别拟合了计算地板反射比为0.2时的利用系数和地板反射比不为0.2时利用系数修正系数。使用训练好的模型代替传统的利用系数查表过程,降低了照度计算的计算误差,提高了工程实用性。实验结果表明,最大误差率约为2%。
Aiming to the phenomenon that the calculation of utilization factor is complicated and not accurate in traditional illumination calculation, a neural network model optimized by Adam algorithm and consisting of a fixed network and a variable network in parallel is designed and realized to fit the coefficient calculation. The utilization factor when the floor reflectance is 0.2 and the utilization factor correction factor when floor reflectance is not 0.2 are calculated separately. Replacing the traditional look-up process with trained model can reduce calculation error, and improve the engineering practicability. The experimental results show that the maximum error rate is about 2%, far less than the requirements in the standard.
Aiming to the phenomenon that the calculation of utilization factor is complicated and not accurate in traditional illumination calculation, a neural network model optimized by Adam algorithm and consisting of a fixed network and a variable network in parallel is designed and realized to fit the coefficient calculation. The utilization factor when the floor reflectance is 0.2 and the utilization factor correction factor when floor reflectance is not 0.2 are calculated separately. Replacing the traditional look-up process with trained model can reduce calculation error, and improve the engineering practicability. The experimental results show that the maximum error rate is about 2%, far less than the requirements in the standard.
引文
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[2] SHANBHAG R S,CHANDRASHEKARA A S,RADHAKRISHNA A S.Application of neural networks in lighting system design:an evaluation of the utilization factor[J].Journal of Polymer Science A Polymer Chemistry,2004,8(2):563-576.
[3] 江莉,方林宏.基于MATLAB的RBF神经网络在照明计算中的应用[J].照明工程学报,2014(3):121-123.
[4] 焦李成,杨淑媛,刘芳,等.神经网络七十年:回顾与展望[J].计算机学报,2016,39(08):1697-1716.
[5] 谢秀颖.电气照明技术[M].北京:中国电力出版社,2004.
[6] KINGMA D P,BA J.Adam:A Method for Stochastic Optimization[J].Computer Science,2014.
[7] 高玉明,张仁津.基于遗传算法和BP神经网络的房价预测分析[J].计算机工程,2014,40(4):187-191.
[8] 郑绪枝,雷靖,夏薇.基于快速确定隐层神经元数的BP神经网络算法[J].计算机科学,2012,39(6):432-436.