Abstract
This paper studies the factors that affect the generalization ability of a neural network model. Take an example of alumina concentration soft sensing in the process of aluminum electrolysis, some measures are presented to improve the model's generalization ability. They include constructing neural networks with prior knowledge, ensuring the quantity and quality of samples through the special experiments and training neural networks both off-line and on-line. The practical application shows their effectiveness. The neural network model based on these design methods proved to be precise. It has better generalization ability and provides a reliable guarantee for advanced process control.
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