Model of Online Grain Moisture Test System Based on Improved BP Neural Network

This paper introduces the multiple sensing mechanism of online moisture test for grain and improved BP neural network. 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. This model overcomes single sensor detection and the method of single curve fitting. Using multi-sensor detection and data processing with neural network, and comprehensively considering the effect on testing output with the aim sensor characteristic and non-aim parameter, the measurement accuracy obtains a great enhancement. The sample testing experiment shows that the model established in this paper has high measurement precision and good reproducibility.