Optimization of the operational parameters in a fast axial flow CW CO2 laser using artificial neural networks and genetic algorithms
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 lear...
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| Published in | Optics and laser technology Vol. 40; no. 8; pp. 1000 - 1007 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Elsevier Science
01.11.2008
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0030-3992 |
| DOI | 10.1016/j.optlastec.2008.03.003 |
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| 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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| AbstractList | 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. |
| Author | Dehghan, G.H. Aghanajafi, C. Jelvani, S. Adineh, V.R. |
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| Cites_doi | 10.1109/72.329697 10.1007/BF00620907 10.1088/0022-3727/26/4/005 10.1016/S0030-3992(01)00116-5 10.1088/0022-3727/20/6/004 10.1088/0022-3727/27/3/006 10.1016/j.optlastec.2007.07.016 10.1162/neco.1995.7.2.219 10.1016/0925-2312(95)00094-1 10.1117/1.2360997 10.1088/0022-3727/26/4/006 10.1088/0022-3727/26/11/007 10.1016/S0030-3992(01)00081-0 |
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| Keywords | Electrical pumping Neural networks Genetic algorithm Electric discharges Optimization method Artificial neural networks Experimental study Gas lasers Fast-axial-flow CW CO2 laser Axial flow Genetic algorithms Carbon dioxide lasers |
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| SubjectTerms | Computers in experimental physics Exact sciences and technology Fundamental areas of phenomenology (including applications) Gas lasers including excimer and metal-vapor lasers Instruments, apparatus, components and techniques common to several branches of physics and astronomy Lasers Neural networks, fuzzy logic, artificial intelligence Optics Physics |
| Title | Optimization of the operational parameters in a fast axial flow CW CO2 laser using artificial neural networks and genetic algorithms |
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