A neural network model of erbium-doped photonic crystal fibre amplifiers

The evaluation of the general evolution equations that describe the longitudinal propagation of pump, signal, forward and backward amplified spontaneous emission in rare-earth-doped optical fibre amplifier could be computationally expensive. In this paper, to reduce the computational time, a neural...

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Bibliographic Details
Published in:Optics and laser technology 2009-07, Vol.41 (5), p.580-585
Main Authors: Fornarelli, G., Mescia, L., Prudenzano, F., De Sario, M., Vacca, F.
Format: Article
Language:English
Subjects:
Artificial neural networks
Computers in experimental physics
Exact sciences and technology
Fiber optics
Fundamental areas of phenomenology (including applications)
Instruments, apparatus, components and techniques common to several branches of physics and astronomy
Laser optical systems: design and operation
Neural modeling
Neural networks, fuzzy logic, artificial intelligence
Optics
Photonic crystal fibre amplifiers
Physics
Resonators, cavities, amplifiers, arrays, and rings
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Summary:The evaluation of the general evolution equations that describe the longitudinal propagation of pump, signal, forward and backward amplified spontaneous emission in rare-earth-doped optical fibre amplifier could be computationally expensive. In this paper, to reduce the computational time, a neural network approach for the modeling of erbium-doped photonic crystal fibre amplifiers is proposed. A number of simulations have been performed to investigate the characteristics of the proposed approach. The numerical results show good agreement between the neural network approach and the conventional algorithm based on the solution of the power evolution equations. The proposed approach exhibits attractive performance in terms of flexibility, accuracy and computational time.
ISSN:0030-3992
1879-2545
DOI:10.1016/j.optlastec.2008.10.010