摘要
粒子群优化(Particle Swarm Optimization,PSO)算法、蝠鲼觅食优化(Manta Ray Foraging Optimiza-tion,MRFO)算法和海洋捕食者算法(Marine Predator Algorithm,MPA)被广泛应用于光纤布拉格光栅(fiber Bragg grating,FBG)重叠光谱解调。针对两个FBG重叠光谱的情况,通过数值仿真实验,深入探究了算法关键参数(包括光谱重叠程度、算法初始种群数和迭代次数)对解调误差的影响规律,以及算法解调误差和信号噪声大小的关系。以平均解调误差为评价指标,在定量分析基础上给出了关键参数合理的设置范围。仿真结果表明:光谱重叠程度大约在30%,算法种群数在40~80,算法迭代次数位于60~90时,优化算法的解调性能最优。在优化条件下,FBG光谱复用解调性能得以提升,节省了时间成本。
Particle swarm optimization(PSO)algorithm,manta ray foraging optimization(MRFO)algorithm and marine predator(MPA)algorithm are widely used in fiber Bragg grating(FBG)overlapping spectral demodulation.In view of the overlapping spectra of two FBGs,this paper deeply explores the influence of key parameters of the algorithm(including the degree of spectral overlap,the initial population number and the number of iterations)on the demodu-lation error,and the relationship between the demodulation error and the signal noise through numerical simulation ex-periments.The average demodulation error is used as an evaluation index,and a reasonable setting range of key pa-rameters is given on the basis of quantitative analysis.The simulation results showed that the demodulation performance of the optimization algorithm is optimal when the degree of spectral overlap is about 30%,the algorithm population is 40-80,and the algorithm iteration times are 60-90.Under the optimized conditions,the performance of FBG spectral multiplexing demodulation is improved and time cost is saved.
作者
尚秋峰
刘峰
SHANG Qiufeng;LIU Feng(Department of Electronic and Communication Engineering,North China Electric Power University,Baoding Hebei 071003,China;Hebei Key Laboratory of Power Internet of Things Technology,North China Electric Power University,Baoding Hebei 071003,China;Baoding Key Laboratory of Optical Fiber Sensing and Optical Communication Technology,North China Electric Power University,Baoding Hebei 071003,China)
出处
《激光杂志》
CAS
北大核心
2023年第8期130-133,共4页
Laser Journal
基金
国家自然科学基金(No.61775057)
河北省自然科学基金(No.E2019502179)。
关键词
光纤布拉格光栅
重叠光谱
优化算法
解调
参数优化
fiber bragg grating
overlapping spectra
optimization algorithms
demodulation
parameter optimization
