ニューラルネットワークを用いたFBGセンサによる歪み計測 : 誤差逆伝搬法によるシミュレーション

概要

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Recently, a variety of sensors using optical fibers have been studied intensively since thesesensors offer a compact, robust, lightweight and potentially low cost solution. In this paper, weconcentrate an strain sensors using fiber Bragg grating (FBG) because FBG sensors bave provento be useful for monitoring structural strains (elongation). However, most of the FBG sensorsreported so far employ a spectrum analyzer that is usually expensive. We propose here a low costFBG sensor utilizing artificial neural networks that is used to learn the mapping relalion betweenstrains and normalized trartsmission power spectrum. Numerical simulation based on a backpropagation method demonstrates that the FBG sensors with neural networks are useful for themeasurements of the strain.
室蘭工業大学の論文
2002-11-30