Abstract
This work presents adaptive genetic-neural network for sinter's burning through point (BTP), since BTP control is most important, which is tightly coupled with sinter ore quality. In off-line, the adaptive genetic algorithm (AGA) is used to optimize the connection weights and thresholds, and during on-line hybrid neural network (HNN) inherited from the principle of back propagation is used to train the map parameters and improve the system precision in each sampling period. The results obtained from the actual process demonstrate that the performance and capability of the proposed system are superior.
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