A Nonlinear ANC System with a SPSA-Based Recurrent Fuzzy Neural Network Controller

Authors:
Qizhi Zhang;Yali Zhou;Xiaohe Liu;Xiaodong Li;Woonseng Gan
Affiliations:
Department of Computer Science and Automation, Beijing Institute of Machinery, P. O. Box 2865, Beijing, 100085, China;Department of Computer Science and Automation, Beijing Institute of Machinery, P. O. Box 2865, Beijing, 100085, China;Department of Computer Science and Automation, Beijing Institute of Machinery, P. O. Box 2865, Beijing, 100085, China;Institute of Acoustics, Academia Sinica, China;School of EEE, Nanyang Technological University, Singapore
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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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.