Functional link artificial neural network applied to active noise control of a mixture of tonal and chaotic noise |
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| Affiliation: | 1. Process Engineering and Instrumentation Cell, CSIR-Institute of Minerals and Materials Technology, Bhubaneswar 751013, India;2. Department of Electrical Engineering, National Institute of Technology, Rourkela, India;1. Department of Electrical Engineering, COMSATS Institute of Information Technology, Park Road, Chak Shahzad, Islamabad 44000, Pakistan;2. Graduate School of Decision Science and Technology, Tokyo Institute of Technology, 2-12-1-W9 Ookayama, Meguro-ku, Tokyo 152-8552, Japan;1. National Engineering Laboratory for Speech and Language Information Processing, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230026, China;2. Department of Electrical Engineering, COMSATS Institute of Information Technology, Attock, Pakistan;3. Department of Electrical Engineering, International Islamic University, Islamabad, Pakistan;1. Key Laboratory of Modern Acoustics and Institute of Acoustics, Nanjing University, Nanjing 210093, China;2. Department of Mechanics and Acoustics, National Institute of Metrology, Beijing 100029, China;3. Centre for Audio, Acoustics and Vibration, Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia |
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| Abstract: | Many practical noises emanating from rotating machines with blades generate a mixture of tonal and the chaotic noise. The tonal component is related to the rotational speed of the machine and the chaotic component is related to the interaction of the blades with air. An active noise controller (ANC) with either linear algorithm like filtered-X least mean square (FXLMS) or nonlinear control algorithm like functional link artificial neural network (FLANN) or Volterra filtered-X LMS (VFXLMS) algorithm shows sub-optimal performance when the complete noise is used as reference signal to a single controller. However, if the tonal and the chaotic noise components are separated and separately sent to individual controller with tonal to a linear controller and chaotic to a nonlinear controller, the noise canceling performance is improved. This type of controller is termed as hybrid controller. In this paper, the separation of tonal and the chaotic signal is done by an adaptive waveform synthesis method and the antinoise of tonal component is produced by another waveform synthesizer. The adaptively separated chaotic signal is fed to a nonlinear controller using FLANN or Volterra filter to generate the antinoise of the chaotic part of the noise. Since chaotic noise is a nonlinear deterministic noise, the proposed hybrid algorithm with FLANN based controller shows better performance compared to the recently proposed linear hybrid controller. A number of computer simulation results with single and multitone frequencies and different types of chaotic noise such as logistic and Henon map are presented in the paper. The proposed FLANN based hybrid algorithm was shown to be performing the best among many previously proposed algorithms for all these noise cases including recorded noise signal. |
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| Keywords: | Active noise control (ANC) Narrowband ANC Chaotic noise Hybrid ANC Functional link artificial neural network (FLANN) |
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