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Neural networks (NNs) have been widely implemented for identifying nonlinear models, and predicting the distribution of targets, due to their ability to store and learn training samples. However, for highly complex systems, it is difficult to build a robust global network model, and efficiently managing the large amounts of experimental data is often required in real-time applications. In this paper,...
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