Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
739718 | Optics & Laser Technology | 2008 | 8 Pages |
Abstract
This paper presents an artificial intelligence approach for optimization of the operational parameters such as gas pressure ratio and discharge current in a fast-axial-flow CW CO2 laser by coupling artificial neural networks and genetic algorithm. First, a series of experiments were used as the learning data for artificial neural networks. The best-trained network was connected to genetic algorithm as a fitness function to find the optimum parameters. After the optimization, the calculated laser power increases by 33% and the measured value increases by 21% in an experiment as compared to a non-optimized case.
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Authors
V.R. Adineh, C. Aghanajafi, G.H. Dehghan, S. Jelvani,