船舶舱室照明系统会受到多种因素的影响，这些因素往往具有模糊性和不确定性，传统神经网络不具备有效处理上述问题的能力，因此，提出模糊神经网络下船舶舱室照明亮度自动调节方法。采集船舶舱室亮度数据，利用自适应加权算法得到清洗后亮度数据，并通过余弦定律获得各角度照明亮度。将得到的亮度参数输入至模糊神经网络中进行模糊化处理，转换为模糊语言变量，根据专家知识或经验建立模糊规则库，并根据输入的模糊语言变量和模糊规则库进行模糊推理，得到模糊输出，将模糊输出转换为具体的亮度调节值，通过去模糊化后的输出值调节船舶舱室的照明亮度。实验结果证明所提方法能够完成船舶舱室照明亮度的自动调节，保证舱室内照明亮度的舒适度。
The lighting system of ship cabins is affected by various factors, which often have fuzziness and uncertainty. Traditional neural networks do not have the ability to effectively deal with the above problems. Therefore, a method for automatically adjusting the brightness of ship cabin lighting under fuzzy neural networks is proposed. Collect ship cabin brightness data, use adaptive weighting algorithm to obtain cleaned brightness data, and use cosine law to obtain illumination brightness at various angles. Input the obtained brightness parameters into a fuzzy neural network for fuzzification processing, convert them into fuzzy language variables, establish a fuzzy rule library based on expert knowledge or experience, and perform fuzzy inference based on the input fuzzy language variables and fuzzy rule library to obtain fuzzy output. Convert the fuzzy output into specific brightness adjustment values, and adjust the lighting brightness of the ship cabin through the deblurring output values. The experimental results demonstrate that the proposed method can automatically adjust the brightness of ship cabin lighting, ensuring the comfort of cabin lighting brightness.
2025,47(9): 84-88 收稿日期：2024-9-2
DOI：10.3404/j.issn.1672-7649.2025.09.015
分类号：TP183
基金项目：江苏省高等职业院校专业带头人高端研修资助项目（2023GRFX017）
作者简介：海光美（1983-） 女，硕士，副教授，研究方向为电气自动化与建筑智能化
参考文献：
[1] 诸柏文, 章晶. 动态环境恒照度客室照明控制技术应用研究[J]. 城市轨道交通研究, 2022(S1): 48-50+54.
ZHU B W, ZHANG J. Application of constant illumination control technology in dynamic environment of train passenger compartment[J]. Urban Mass Transit, 2022(S1): 48-50+54.
[2] 许馨尹, 刘梦杰, 李涛, 等. 基于日光估计的动态照明控制方法研究[J]. 建筑科学, 2022, 38(8): 184-193.
XU X Y, LIU M J, LI T, et al. Research on dynamic illumination control method based on daylight estimation[J]. Building Science, 2022, 38(8): 184-193.
[3] 姚剑, 史天运, 端嘉盈. 铁路客站站台照明节能控制策略研究[J]. 铁道运输与经济, 2022, 44(8): 58-64+90.
YAO J, SHI T Y, DUAN J Y. Research on energy-saving control strategy of platform lighting in railway passenger station[J]. Railway Transport and Economy, 2022, 44(8): 58-64+90.
[4] 梁波, 牛佳安, 李硕, 等. 考虑能见度影响的公路隧道照明动态优化与智能控制[J]. 控制理论与应用, 2023, 40(10): 1783-1792.
LIANG B, NIU J A, LI S, et al. Dynamic optimization and intelligent control of highway tunnel lighting considering visibility effects[J]. Control Theory & Applications, 2023, 40(10): 1783-1792.
[5] CHEN Z , LI M , FAN L S . An intelligent lighting control system for urban street lamps using internet of things[J]. Mobile information systems, 2022, 2022(25): 5678532.1-5678532.10.
[6] 张玉杰, 王帆. 基于改进麻雀搜索算法的照明控制优化[J]. 计算机应用, 2023, 43(3): 835-841.
ZHANG Y J, WANG F. Lighting control optimization based on improved sparrow search algorithm[J]. Journal of Computer Applications, 2023, 43(3): 835-841.
[7] 郭敬, 张玉杰. 基于遗传模拟退火算法的室内照明节能控制方法研究[J]. 应用光学, 2022, 43(5): 879-885.
GUO J, ZHANG Y J. Indoor lighting energy-saving control method based on genetic simulated annealing algorithm[J]. Journal of Applied Optics, 2022, 43(5): 879-885.
[8] 赵钊. 基于光子计数法的室内照明照度智能控制系统[J]. 激光杂志, 2022, 43(7): 221-225.
ZHAO Z. Intelligent control system of indoor illumination based on photon counting method[J]. Laser Journal, 2022, 43(7): 221-225.
[9] 曾茂庭, 楼俊, 岑松原, 等. 基于扩展光源的双自由曲面均匀照明设计方法[J]. 激光与光电子学进展, 2023, 60(19): 180-184.
ZENG M T, LOU J, CEN S Y, et al. Design method for double free-form surface with uniform illumination based on an extended light source[J]. Laser & Optoelectronics Progress, 2023, 60(19): 180-184.
[10] 吴艳丽, 揭安仝. 基于视觉参照系重构的室内照明区域控制仿真[J]. 计算机仿真, 2023, 40(6): 518-522.
WU Y L, JIE A T. Simulation of indoor lighting area control based on reconstruction of visual reference frame[J]. Computer Simulation, 2023, 40(6): 518-522.
[11] 陈寒梅, 于春荣, 刘智超. 基于深度学习的室内照明智能调节系统[J]. 红外与激光工程, 2022, 51(7): 499-504.
CHEN H M, YU C R, LIU Z C. Intelligent adjustment system of indoor lighting based on deep learning[J]. Infrared and Laser Engineering, 2022, 51(7): 499-504.
[12] 李自森, 毛馨凯, 王洪亮. 选煤厂智能照明控制[J]. 工矿自动化, 2022, 48(S1): 124-125+132.
LI Z S, MAO X K, WANG H L. Intelligent lighting control for coal preparation plant[J]. Journal of Mine Automation, 2022, 48(S1): 124-125+132.
[13] 杨云, 刘晨, 丁鹏鸽, 等. 隧道多源模糊自适应智能照明网络设计与实现[J]. 地下空间与工程学报, 2023, 19(S2): 955-962+970.
YANG Y, LIU C, DING P G, et al. Design and implementation of tunnel multi-source fuzzy adaptive intelligent lighting network[J]. Chinese Journal of Underground Space and Engineering, 2023, 19(S2): 955-962+970.
[14] 高金武, 王向阳, 贾志桓, 等. 点阵式LED智能前照灯控制功能算法研究[J]. 汽车技术, 2023(5): 51-56.
GAO J W, WANG X Y, JIA Z H, et al. Research on control function algorithm of dot matrix LED intelligent headlamp[J]. Automobile Technology, 2023(5): 51-56.
[15] 何湘桂, 孟志强, 周华安. 基于LoRa物联网的城市道路照明能耗与窃电监测系统[J]. 湖南大学学报(自然科学版), 2023, 50(10): 11-19.
HE X G, MENG Z Q, ZHOU H A. System of urban road lighting energy consumption and power theft monitoring based on lora internet of things[J]. Journal of Hunan University: Natural Sciences, 2023, 50(10): 11-19.
