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Optimal design of multi-channel fiber Bragg grating filters based on particle swarm optimization algorithm
Liu, Tundong ; Ye, Zengruan ; Chen, Jing ; Tao, Jiping ; Jiang, ; Liu TD(刘暾东) ; Tao JP(陶继平)
刊名http://dx.doi.org/10.3788/AOS201434.0806005
2014
关键词Algorithms Design Fiber Bragg gratings Fiber optics Group delay Mathematical models Modulation Parameter estimation Refractive index
英文摘要With the increasing number of channels, fabricating a multi-channel fiber Bragg grating (FBG) filter requires a considerably high maximum index modulation, which is beyond a physically realizable level. Hence, an effective optimization method based on particle swarm optimization (PSO) algorithm and direct design method to design a multi-channel FBG filter is proposed. By introducing the tailored group delay coefficients into the target reflectivity spectrum, a mathematical model aiming at minimizing the maximum index modulation of the grating is established. The PSO algorithm is employed to find the optimal group delay parameter for each channel. It leads to a more even distribution of the refractive index modulation, thereby reduces the maximum index modulation to a physically realizable level. Design examples of the 40-channel and 106-channel FBG filters both with uniform reflection spectra demonstrate that the proposed method yields a remarkable reduction in the maximum index modulation which is below 0.001.
语种英语
出版者Chinese Optical Society
内容类型期刊论文
源URL[http://dspace.xmu.edu.cn/handle/2288/92862]  
专题信息技术-已发表论文
推荐引用方式
GB/T 7714
Liu, Tundong,Ye, Zengruan,Chen, Jing,et al. Optimal design of multi-channel fiber Bragg grating filters based on particle swarm optimization algorithm[J]. http://dx.doi.org/10.3788/AOS201434.0806005,2014.
APA Liu, Tundong.,Ye, Zengruan.,Chen, Jing.,Tao, Jiping.,Jiang,.,...&陶继平.(2014).Optimal design of multi-channel fiber Bragg grating filters based on particle swarm optimization algorithm.http://dx.doi.org/10.3788/AOS201434.0806005.
MLA Liu, Tundong,et al."Optimal design of multi-channel fiber Bragg grating filters based on particle swarm optimization algorithm".http://dx.doi.org/10.3788/AOS201434.0806005 (2014).
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