Optimal Design of Gain-Flattened Raman Fiber Amplifiers Using a Hybrid Approach Combining Randomized Neural Networks and Differential Evolution Algorithm
An efficient method based on a hybrid approach that combines extreme learning machine (ELM) technique and differential evolution (DE) algorithm is proposed to optimize the multipumped Raman fiber amplifier (RFA). The proposed method takes advantage of the fast learning speed and high generalization...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
|
| Series: | IEEE Photonics Journal |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8320966/ |
| _version_ | 1818618617023954944 |
|---|---|
| author | Jing Chen Hao Jiang |
| author_facet | Jing Chen Hao Jiang |
| author_sort | Jing Chen |
| collection | DOAJ |
| description | An efficient method based on a hybrid approach that combines extreme learning machine (ELM) technique and differential evolution (DE) algorithm is proposed to optimize the multipumped Raman fiber amplifier (RFA). The proposed method takes advantage of the fast learning speed and high generalization of the ELM as well as the strong global search capability of DE. From a novel perspective, we utilize ELM as a powerful learning tool to construct the nonlinear mapping between the pump parameters and gains of RFA. Instead of time-consuming integration of Raman coupled equations, the gains can be directly and accurately determined by the ELM model. To obtain a flat gain spectrum, DE algorithm is employed to find the optimal wavelengths and powers of pumps. The well-trained ELM model is incorporated into the evolution of DE to accelerate the search process. The results show that the designed RFAs with the optimized pump parameters achieve the desired gain performance and meanwhile maintain very low level of gain ripple. In comparison to other related methods, the proposed method significantly shortens the computation time and enhances the overall optimization efficiency, which offers potential for real-time adjustment and flexibility of RFA design. |
| first_indexed | 2024-12-16T17:24:26Z |
| format | Article |
| id | doaj.art-34fc7443cf54424bb2212287aec9a935 |
| institution | Directory Open Access Journal |
| issn | 1943-0655 |
| language | English |
| last_indexed | 2024-12-16T17:24:26Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj.art-34fc7443cf54424bb2212287aec9a9352022-12-21T22:23:05ZengIEEEIEEE Photonics Journal1943-06552018-01-0110211510.1109/JPHOT.2018.28178438320966Optimal Design of Gain-Flattened Raman Fiber Amplifiers Using a Hybrid Approach Combining Randomized Neural Networks and Differential Evolution AlgorithmJing Chen0https://orcid.org/0000-0001-8305-9291Hao Jiang1https://orcid.org/0000-0002-6902-9245College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, ChinaFujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou, ChinaAn efficient method based on a hybrid approach that combines extreme learning machine (ELM) technique and differential evolution (DE) algorithm is proposed to optimize the multipumped Raman fiber amplifier (RFA). The proposed method takes advantage of the fast learning speed and high generalization of the ELM as well as the strong global search capability of DE. From a novel perspective, we utilize ELM as a powerful learning tool to construct the nonlinear mapping between the pump parameters and gains of RFA. Instead of time-consuming integration of Raman coupled equations, the gains can be directly and accurately determined by the ELM model. To obtain a flat gain spectrum, DE algorithm is employed to find the optimal wavelengths and powers of pumps. The well-trained ELM model is incorporated into the evolution of DE to accelerate the search process. The results show that the designed RFAs with the optimized pump parameters achieve the desired gain performance and meanwhile maintain very low level of gain ripple. In comparison to other related methods, the proposed method significantly shortens the computation time and enhances the overall optimization efficiency, which offers potential for real-time adjustment and flexibility of RFA design.https://ieeexplore.ieee.org/document/8320966/Raman fiber amplifieroptimizationgain flatnessmachine learning. |
| spellingShingle | Jing Chen Hao Jiang Optimal Design of Gain-Flattened Raman Fiber Amplifiers Using a Hybrid Approach Combining Randomized Neural Networks and Differential Evolution Algorithm IEEE Photonics Journal Raman fiber amplifier optimization gain flatness machine learning. |
| title | Optimal Design of Gain-Flattened Raman Fiber Amplifiers Using a Hybrid Approach Combining Randomized Neural Networks and Differential Evolution Algorithm |
| title_full | Optimal Design of Gain-Flattened Raman Fiber Amplifiers Using a Hybrid Approach Combining Randomized Neural Networks and Differential Evolution Algorithm |
| title_fullStr | Optimal Design of Gain-Flattened Raman Fiber Amplifiers Using a Hybrid Approach Combining Randomized Neural Networks and Differential Evolution Algorithm |
| title_full_unstemmed | Optimal Design of Gain-Flattened Raman Fiber Amplifiers Using a Hybrid Approach Combining Randomized Neural Networks and Differential Evolution Algorithm |
| title_short | Optimal Design of Gain-Flattened Raman Fiber Amplifiers Using a Hybrid Approach Combining Randomized Neural Networks and Differential Evolution Algorithm |
| title_sort | optimal design of gain flattened raman fiber amplifiers using a hybrid approach combining randomized neural networks and differential evolution algorithm |
| topic | Raman fiber amplifier optimization gain flatness machine learning. |
| url | https://ieeexplore.ieee.org/document/8320966/ |
| work_keys_str_mv | AT jingchen optimaldesignofgainflattenedramanfiberamplifiersusingahybridapproachcombiningrandomizedneuralnetworksanddifferentialevolutionalgorithm AT haojiang optimaldesignofgainflattenedramanfiberamplifiersusingahybridapproachcombiningrandomizedneuralnetworksanddifferentialevolutionalgorithm |