Application of Improved BP Neural Networks Based on LM Algorithm in Characteristic Curve Fitting of Fiber-Optic Micro-Bend Sensor

Shuo Ding; Xiao Heng Chang

doi:10.4028/www.scientific.net/AMR.889-890.825

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 889-890Application of Improved BP Neural Networks Based...

Application of Improved BP Neural Networks Based on LM Algorithm in Characteristic Curve Fitting of Fiber-Optic Micro-Bend Sensor

Abstract:

As a key factor in a testing system, sensor nonlinearity has always been the study focus in the field of engineering and techniques. In order to accurately reflect the practical characteristics of a fiber-optic micro-bend sensor, Levenberg-Marguardt (LM) algorithm is used to optimize the correction of the weight values of standard back propagation neural network (BPNN). The learning process of improved BPNN based on LM algorithm (LM-BPNN) is also illustrated mathematically, and LM-BPNN is applied in fitting the input and output characteristic curve of a fiber-optic micro-bend sensor. The simulation results show that LM-BPNN is superior both in its convergence rate and fitting precision over standard BPNN.

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Periodical:

Advanced Materials Research (Volumes 889-890)

Pages:

825-828

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.889-890.825

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Online since:

February 2014

Authors:

Shuo Ding*, Xiao Heng Chang

Keywords:

Back Propagation (BP) Neural Network, Curve Fitting, Fiber-Optic Micro-Bend Sensor, LM Algorithm, Simulation

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* - Corresponding Author

References

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