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

Abstract

This paper introduces the multiple sensing mechanism of online moisture test for grain and improved BP neural network, and gives hardware structure of moisture test system for grain centering on TMS320F2812. 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 grain 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.