Abstract
In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized Inverse Learning Architecture(GILA), the Specialized Inverse Learning Architecture(SILA)- I & II and the Error Back Propagat Model(EBPM) are adopted respectively to simulate the static and dynamic welding control processes. The results of simulation show that the SILA-I and EBPM have better properties. The factors affecting the simulating results and the dynamic response quality have also been analyzed
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© 2007 Springer-Verlag Berlin Heidelberg
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Ding, F., Yu, S., Jianjun, L., Yuezhou, M., Jianhong, C. (2007). Computer Simulation of Neural Network Control System for CO2 Welding Process. In: Tarn, TJ., Chen, SB., Zhou, C. (eds) Robotic Welding, Intelligence and Automation. Lecture Notes in Control and Information Sciences, vol 362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73374-4_13
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DOI: https://doi.org/10.1007/978-3-540-73374-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73373-7
Online ISBN: 978-3-540-73374-4
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