Grain Quality Evaluation Method Based on Combination of BP Neural Networks with D-S Evidence Theory

Abstract

Aiming at the shortcomings of the BP neural network, this paper presents a method for grain condition information fusion based on BP neural networks and D-S evidential theory. This method firstly employs many BP neural network outputs as the inputs of D-S evidence theory. After that, D-S evidence theory is used to fuse with results from all the neural networks, resulting in the grain quality evaluation. Numerical example shows that proposed method improves the various mathematical and computational techniques have been developed and adapted to manipulate and interpret motion data, exactitude and decreases the recognition uncertainty.