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Data fusion was used to predict the safe value of grain environment in different operational conditions. The temperature, humidity, moisture, pest and etc., were used as inputs to the data fusion center and network. Feed-forward artificial neural networks with 4-10-5 arrangements were capable to estimate the safe value of grain environment as the outputs. This method, characterized by sufficiently...
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...
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