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Active Noise Cancellation Using System Identification and Control Based on Neural Network

Ghasri, Homa | 2009

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 40429 (55)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Haj Sadeghi, Khosro
  7. Abstract:
  8. This thesis presents a new approach to feedback active noise cancellation. The problem of Reducing the noise level in the environment has been the focus of research over the years. Active Noise Cancellation (ANC) is one such approach that has been proposed to eliminate Noise. ANC refers to an electro acoustic approach of canceling acoustic disturbance to Produce a quieter environment. The basic principle of ANC is to introduce a canceling “anti Noise” signal that has the same amplitude but the exact opposite phase, thus resulting in Reducing the remainder noise signal. We use neural networks based on system identification And predict control to cancel acoustic noise. Canceling acoustic noise algorithm is based on Feedback model of active noise cancellation (ANC) theory. Canceling noise algorithm considers a Neural Network Predictor Control (NNPC). This predictor is one MLP neural Network with six neurons in input layer and five neurons in hidden layer and one neuron in Output layer. It uses NARX predictor model for system identification theory and it is trained Using Levenberge_Marquart training. We studied our proposed method by simulating it and Simulation results show significant reduction in noise.
  9. Keywords:
  10. Neural Network ; System Identification ; Active Noise Cancellation (ANC) ; Neural Network Predictor Control ; Electro Acoustic

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