Abstract
In this paper, a feedforward active noise control (ANC) system using a recurrent fuzzy neural network (RFNN) controller based on simultaneous perturbation stochastic approximation (SPSA) algorithm is considered. Because RFNN can capture the dynamic behavior of a system through the feedback links, only one input node is needed, and the exact lag of the input variables need not be known in advance. The SPSA-based RFNN control algorithm employed in the ANC system is first derived. Following this, computer simulations are carried out to verify that the SPSA-based RFNN control algorithm is effective for a nonlinear ANC system. Simulation results show that the proposed scheme is able to significantly reduce disturbances without the need to model the secondary-path and has better tracking ability under variable secondary-path. This observation implies that the SPSA-based RFNN controller eliminates the need of the modeling of the secondary-path.
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References
Nelson, P.A., Elliott, S.J.: Active Sound Control. Academic Press, London (1991)
Snyder, S.D., Tanaka, N.: Active Control of Vibration using a Neural Network. IEEE Trans. On Neural Networks 6(4), 819–828 (1995)
Maeda, Y., De Figueiredo, R.J.P.: Learning Rules for Neuro-Controller via Simultaneous Perturbation. IEEE Transactions On Neural Networks 8(5), 1119–1130 (1997)
Zhou, Y.L., Zhang, Q.Z., Li, X.D., Gan, W.S.: Analysis and DSP Implementation of an ANC System using a Filtered-Error Neural Network. Journal of Sound and Vibration 285(1), 1–25 (2005)
Spall, J.C.: Multivariate Stochastic Approximation using Simultaneous Perturbation Gradient Approximation. IEEE Transactions On Automatic Control 37(3), 332–341 (1992)
Maeda, Y., Yoshida, T.: An Active Noise Control without Estimation of Secondary-Path. In: ACTIVE1999, USA, pp. 985–994 (1999)
Zhou, Y.L., Zhang, Q.Z., Li, X.D., Gan, W.S.: Model-Free Control of a Nonlinear ANC System with a SPSA-based Neural Network Controller. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 1033–1038. Springer, Heidelberg (2006)
Zhang, Q.Z., Gan, W.S., Zhou, Y.L.: Adaptive Recurrent Fuzzy Neural Networks for Active Noise Control. Journal of Sound and Vibration 296, 935–948 (2006)
Spall, J.C., Cristion, J.A.: A Neural Network Controller for Systems with Unmodeled Dynamics with Applications to Wastewater Treatment. IEEE Transactions on Systems. Man. And Cybernetics 27(3), 369–375 (1997)
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Zhang, Q., Zhou, Y., Liu, X., Li, X., Gan, W. (2007). A Nonlinear ANC System with a SPSA-Based Recurrent Fuzzy Neural Network Controller. 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_22
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DOI: https://doi.org/10.1007/978-3-540-72383-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
Online ISBN: 978-3-540-72383-7
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