Towards intelligent fiber laser design by using a feed-forward neural network

被引：0
|
作者：
Liu, Xinyang [1 ]
Gumenyuk, Regina [1 ,2 ]
机构：
[1] Tampere Univ, Lab Photon, Korkeakoulunkatu 3, Tampere 33720, Finland
[2] Tampere Univ, Tampere Inst Adv Study, Kalevantie 4, Tampere 33100, Finland
来源：
ADVANCED LASERS, HIGH-POWER LASERS, AND APPLICATIONS XIV | 2023年 / 12760卷
关键词：
Intelligent laser cavity design; feed-forward neural network; laser output prediction;
D O I：
10.1117/12.2686809
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
We demonstrated a high accuracy prediction of the fiber laser output parameters by using a feed-forward neural network. We explored both the gain and spectral filter parameters to test the prediction performance of the neural network and realized the mapping between cavity parameters and laser output performance. We also investigated how the number of hidden layers could influence the accuracy of prediction. Based on the results, the output spectrum and temporal pulse profiles can be predicted with high accuracy in various fiber laser designs. Our work paves the way to intelligent laser design with ultimate autonomy.
引用
收藏
页数：4
相关论文
共 50 条
  • [41] Characterisation of mobile velocity in GSM using a simple feed-forward neural network
    Balis, PG
    Hinton, OR
    ELECTRONICS LETTERS, 1998, 34 (19) : 1810 - 1811
  • [42] Researching the feed-forward neural network by using the piecewise linear division (PLD)
    Wu, M
    Xu, N
    Wang, JR
    Yang, GZ
    PROCEEDINGS OF THE 3RD WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-5, 2000, : 842 - 846
  • [43] Sleep Apnea Detection Using a Feed-Forward Neural Network on ECG Signal
    da Silva Pinho, Andre Miguel
    Pombo, Nuno
    Garcia, Nuno M.
    2016 IEEE 18TH INTERNATIONAL CONFERENCE ON E-HEALTH NETWORKING, APPLICATIONS AND SERVICES (HEALTHCOM), 2016, : 277 - 282
  • [44] SEFNN-A feed-forward neural network design algorithm based on structure evolution
    National Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
    不详
    Jisuanji Yanjiu yu Fazhan, 2006, 10 (1713-1718):
  • [45] NORMALIZED DATA BARRIER AMPLIFIER FOR FEED-FORWARD NEURAL NETWORK
    Fuangkhon, P.
    NEURAL NETWORK WORLD, 2021, 31 (02) : 125 - 157
  • [46] Loss Surface Modality of Feed-Forward Neural Network Architectures
    Bosman, Anna Sergeevna
    Engelbrecht, Andries Petrus
    Helbig, Marcie
    2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2020,
  • [47] Application of a feed-forward artificial neural network as a mapping device
    Kocjancic, R
    Zupan, J
    JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 1997, 37 (06): : 985 - 989
  • [48] An analog feed-forward neural network with on-chip learning
    Berg, Y
    Sigvartsen, RL
    Lande, TS
    Abusland, A
    ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING, 1996, 9 (01) : 65 - 75
  • [49] Analog feed-forward neural network with on-chip learning
    Univ of Oslo, Oslo, Norway
    Analog Integr Circuits Signal Process, 1 (65-75):
  • [50] Immunohistochemically Dyed Seminiferous Tubules With Feed-Forward Neural Network
    Aydemir, Zubeyr
    Erkaymaz, Okan
    Akpolat Ferah, Mervem
    2017 25TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2017,
12345