Learning evolution design of multiband-transmission fiber Bragg grating filters

Su Frang Shu*, Yin-Chieh Lai, Ci Ling Pan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

A composite fiber Bragg grating (FBG) structure with several apodized sections is utilized for designing dense wavelength division multiplexing (DWDM) multiband transmission filters. A learning genetic algorithm (LGA) is also developed to determine the optimum design parameters of these filters. By taking advantage of a knowledge base (KB) that stores the FBG parameter sets and the corresponding transmission profile feature sets, our LGA can generate a suitable initial population and perform evolutionary optimization starting from it. This has made the LGA evolve more quickly to more accurate results than the methods without using the KB. The LGA can also store new results into the KB according to its decision procedure and improve its precision of initial prediction as it works through more and more examples.

Original languageEnglish
Pages (from-to)2856-2860
Number of pages5
JournalOptical Engineering
Volume42
Issue number10
DOIs
StatePublished - 1 Oct 2003

Keywords

  • Artificial intelligence
  • Fiber Bragg grating
  • Genetic algorithm
  • Optical fiber filter
  • Wavelength division multiplexing

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