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Proceedings of 2009 International Workshop on Information Security and Application (IWISA 2009)

Qingdao, China, November 21-22, 2009

Editors: Feng Gao and Xijun Zhu

AP Catalog Number: AP-PROC-CS-09CN004

ISBN: 978-952-5726-06-0

Page(s): 236-239

Modeling of Neural Network on Moisture Test for Grain Based on Dielectric Loss Factor

Jishun Jiang and Hua Ji

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This paper gives hardware structure of moisture test system for soybean centering on TMS320F2812, and introduces the multiple sensing mechanism of online moisture test for grain and improved BP neural network. In this paper, dielectric loss factor is measured by the method of orthogonal separation on phase-sensitive detection. Though measuring three parameters of dielectric loss factor, power fluctuations and temperature changes, using BP neural network to construct multi-input single-output model, applying gradient descent method with forgetting factor for parameters adjustment of BP neural network, utilizing the nonlinear mapping ability and learning generalization ability of the BP neural network, and using high precision samples for the training of BP neural network, the mathematic model of moisture test system for soybean based on BP neural network was established finally. The sample testing experiment shows that the measurement accuracy obtains a great enhancement comprehensively considering the effect on testing output with the aim sensor characteristic and non-aim parameter. This model overcomes single sensor detection and the method of single curve fitting. Using multi-sensor detection and data processing with neural network, the built model has high measurement precision and good reproducibility.

Index Terms

dielectric loss factor, multi-sensor, BP neural network, soybean moisture, online test

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