Abstract
The accurate control of the work pieces temperature is a nonlinear, large time-delay, and cross-coupling complicated control problem in vacuum annealing furnace. In order to control the temperature of work pieces accurately. The optimization model for accurate work pieces temperature control has been proposed by the data gathered from the scene. The model was set up with Wavelet Neural Networks (WNN). Adaptive Immune Genetic Algorithm (AIGA) optimized the WNN structure and parameters (weights, dilation and translation). Simulation and experiment results show that the model in this paper is better than the model established with NN and optimizing the weights of NN by GA. And, it improves the training rate of Networks and obtains a system with good steady state precision, real timeliness and robustness.
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, X., Liu, D. (2005). Modeling and Optimal for Vacuum Annealing Furnace Based on Wavelet Neural Networks with Adaptive Immune Genetic Algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_129
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DOI: https://doi.org/10.1007/11539117_129
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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