Title :
An application of adaptive genetic-neural algorithm to Sinter´s BTP process
Author_Institution :
Shanghai Univ. of Eng. Sci., China
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.
Keywords :
adaptive systems; backpropagation; control engineering computing; genetic algorithms; neural nets; process control; production engineering computing; sintering; BTP control; adaptive genetic algorithm; adaptive genetic-neural network algorithm; back propagation; hybrid neural network; sinter BTP process; sinter burning through point; sinter ore quality; Adaptive control; Adaptive systems; Fuzzy control; Genetic algorithms; Ignition; Neural networks; Packaging; Predictive models; Temperature control; Velocity control;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1380359