Abstract
A two-stage structure model was developed for the vibration control of an actuator platform and a controller based on a three-layer neural network was applied to realize high performance control for the kickstand disturbance of a block forming machine. This paper presents a survey of the basic theory of the back-propagation(BP) neural network architecture including its architectural design, BP algorithm, the root mean square error (RMSE) and optimal model establishment. The situ-test data of the control system were measured by acceleration transducer and the experimental results indicates that the proposed method was effective.
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References
Ahn, K.G., Pahk, H.J., Jung, M.Y.: A hybrid-type active vibration isolation system using neural networks. Journal of Sound and Vibration 192, 793–805 (1996)
Liu, X.Q., Zhang, H.Y.: Fault Detection and Diagnosis of Permanent-Magnet DC Motor Based on Parameter Estimation and Neural Network. IEEE Transaction on Industrial Electronics 47, 1021–1030 (2000)
Chen, Q., Chan, Y.W., Worden, K.: Structural fault diagnosis and isolation using neural networks based on response-only data. Computers and Structures 81, 2165–2172 (2003)
Zhang, C.L., Mei, D.Q., Chen, Z.C.: Active vibration isolation of a micro-manufacturing platform based on a neural network. Journal of Materials Processing Technology 129, 634–639 (2002)
Zhang, L., Fu, Y.L., He, L.: A New Active Vibration Isolation Control Method Based on CMAC Neural Network. In: IEEE International Conference on Industrial Technology, pp. 1280–1282 (2005)
Samman, M.M.: A Hybrid Analysis Method for Vibration Signals Based on Neural Networks and Pattern Recognition Techniques. Journal of Vibration and Acoustics 123, 122–124 (2001)
Hecht-Nielsen, R.: Theory of the Backpropagation Neural Network. In: International Joint Conference on Neural Networks, vol. 1, pp. 593–650 (1989)
Wu, Y.J., Chau, P.M., Hecht-Nielsen, R.: A Supervised Learning Neural Network Coprocessor for Soft-Decision Maximum-Likelihood Decoding. IEEE Transactions on Neural Networks 6, 986–992 (1995)
Hush, D.R., Horne, B.: Efficient Algorithms for Function Approximation with Piecewise Linear Sigmoidal Networks. IEEE Transactions on Neural Networks 9, 1129–1141 (1998)
Sonmez, H., Gokceoglu, C., Nefeslioglu, H.A., Kayabasi, A.: Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation. International Journal of Rock Mechanics & Mining Sciences 43, 224–235 (2006)
Karri, V.: Drilling Performance Prediction Using General Regression Neural Networks. In: Logananthara, R., Palm, G., Ali, M. (eds.) IEA/AIE 2000. LNCS (LNAI), vol. 1821, pp. 67–73. Springer, Heidelberg (2000)
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Wu, Q., Zhang, Q., Zong, C., Cheng, G. (2007). Vibration Control of Block Forming Machine Based on an Artificial Neural Network. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_28
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DOI: https://doi.org/10.1007/978-3-540-72383-7_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
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