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Applications of BPNN in Analyzing LHM Elemental Basic Structure

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Abstract:

Based on the back propagation multi-layer forward feed neural network, the neural network model is built for the dielectric sensitive structural parameters between the equivalent permittivity and the equivalent permeability, which is used to analyze the basic left-hand materials(LHMS) structural. The experimental results show that the analysis time is 145.535648 seconds and the training mean error is 0.000113426 while adopting the scaled conjugate gradient method. The results are coincident with these ones by the full wave method, satisfying the engineering demand, reducing the faults caused by thickness resonance in the traditional numerical analysis method, realizing the coexistence between the high analysis precision and the high efficiency of the left-hand materials.

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Periodical:

Applied Mechanics and Materials (Volumes 16-19)

Pages:

466-470

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.16-19.466

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Online since:

October 2009

Authors:

Jiang Yu, Xiao Hong, Xing Peng Liu, Wei Teng

Keywords:

BP Neural Network (BPNN), Electromagnetism Characteristic Parameter, Left-Handed Material (LHM), Scaled Conjugate Gradient

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© 2009 Trans Tech Publications Ltd. All Rights Reserved

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