Nonlinear active noise control using EKF-based recurrent fuzzy neural networks

Article type: Research Article

Authors: Bambang, Riyanto T.

Affiliations: School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jl. Ganesha 10, Bandung 40132, Indonesia. Tel.: +62 22 2500960; Fax: +62 22 2534217; E-mail: [email protected]

Abstract: Active Noise Control (ANC) system is commonly designed and implemented using adaptation algorithm and adaptive control structure. In this paper we present theoretical and experimental result of active noise control system using Recurrent Fuzzy Neural Network (RFNN). RFNN is developed by combining fuzzy logic and neural networks, aimed at producing better control system performance than if we use neural network or fuzzy logic separately. Using a control structure with two multilayer feedforward RFNNs (one RFNN serves as a nonlinear controller while the other one operates as a nonlinear plant model), a recursive least-squares algorithm based on Adjoint Extended Kalman Filter approach is employed for the training of the controller network. Extended Kalman Filter (EKF) algorithm is introduced to develop a new algorithm with faster convergence speed by using nonlinear recursive-least square method. Experimental result using DSP demonstrates effectiveness of the proposed RFNN structure and algorithm to attenuate unwanted noise.

Keywords: Active noise control, nonlinearity, recurrent fuzzy neural network, learning algorithm, adjoint extended kalman filter, DSP

DOI: 10.3233/HIS-2007-4405

Journal: International Journal of Hybrid Intelligent Systems, vol. 4, no. 4, pp. 255-267, 2007