Computational neuroscience applied in surface roughness fiber optic sensor

Computational neuroscience has been widely used in fiber optic sensor signal output. This paper introduces a method for processing the Surface Roughness Fiber Optic Sensor output signals with a radial basis function neural network. The output signal of the sensor and the laser intensity signal as th...

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Autor principal:	He Wei
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2019
Materias:	computational neuroscience surface roughness optical fiber sensor radial basis function Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/f37c95ebb36347ff83a34ce3763cb47e
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id	oai:doaj.org-article:f37c95ebb36347ff83a34ce3763cb47e
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spelling	oai:doaj.org-article:f37c95ebb36347ff83a34ce3763cb47e2021-12-05T14:11:04ZComputational neuroscience applied in surface roughness fiber optic sensor2081-693610.1515/tnsci-2019-0012https://doaj.org/article/f37c95ebb36347ff83a34ce3763cb47e2019-04-01T00:00:00Zhttps://doi.org/10.1515/tnsci-2019-0012https://doaj.org/toc/2081-6936Computational neuroscience has been widely used in fiber optic sensor signal output. This paper introduces a method for processing the Surface Roughness Fiber Optic Sensor output signals with a radial basis function neural network. The output signal of the sensor and the laser intensity signal as the light source are added to the input of the RBF neural network at the same time, and with the ability of the RBF neural network to approach the non-linear function with arbitrary precision, to achieve the nonlinear compensation of the sensor and reduction of the effect of changes in laser output light intensity at the same time. The Surface Roughness Fiber Optic Sensor adopting this method has low requirements on the stability of the output power of laser, featuring large measuring range, high accuracy, good repeatability, measuring of special surfaces such as minor area, and the bottom surface of holed etc. The measurements were given and various factors that affect the measurement were analyzed and discussed.He WeiDe Gruyterarticlecomputational neurosciencesurface roughnessoptical fibersensorradial basis functionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENTranslational Neuroscience, Vol 10, Iss 1, Pp 70-75 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	computational neuroscience surface roughness optical fiber sensor radial basis function Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	computational neuroscience surface roughness optical fiber sensor radial basis function Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 He Wei Computational neuroscience applied in surface roughness fiber optic sensor
description	Computational neuroscience has been widely used in fiber optic sensor signal output. This paper introduces a method for processing the Surface Roughness Fiber Optic Sensor output signals with a radial basis function neural network. The output signal of the sensor and the laser intensity signal as the light source are added to the input of the RBF neural network at the same time, and with the ability of the RBF neural network to approach the non-linear function with arbitrary precision, to achieve the nonlinear compensation of the sensor and reduction of the effect of changes in laser output light intensity at the same time. The Surface Roughness Fiber Optic Sensor adopting this method has low requirements on the stability of the output power of laser, featuring large measuring range, high accuracy, good repeatability, measuring of special surfaces such as minor area, and the bottom surface of holed etc. The measurements were given and various factors that affect the measurement were analyzed and discussed.
format	article
author	He Wei
author_facet	He Wei
author_sort	He Wei
title	Computational neuroscience applied in surface roughness fiber optic sensor
title_short	Computational neuroscience applied in surface roughness fiber optic sensor
title_full	Computational neuroscience applied in surface roughness fiber optic sensor
title_fullStr	Computational neuroscience applied in surface roughness fiber optic sensor
title_full_unstemmed	Computational neuroscience applied in surface roughness fiber optic sensor
title_sort	computational neuroscience applied in surface roughness fiber optic sensor
publisher	De Gruyter
publishDate	2019
url	https://doaj.org/article/f37c95ebb36347ff83a34ce3763cb47e
work_keys_str_mv	AT hewei computationalneuroscienceappliedinsurfaceroughnessfiberopticsensor
_version_	1718371454069768192

Computational neuroscience applied in surface roughness fiber optic sensor

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