Shahrood University of Technology Journal of Solid and Fluid Mechanics 2251-9475 3 1 2013 10 15 Periodic Acoustic Noise Cancelation with Intelligent and Active Method Periodic Acoustic Noise Cancelation with Intelligent and Active Method 43 54 174 10.22044/jsfm.2013.174 FA Behrooz Raeisy shiraz university- PhD candidate Journal Article 2013 03 18 In this paper a new method based on temporal difference on reinforcement learning algorithm is proposed to active noise control of periodic acoustic signal. This method does not need dynamic information and therefore it is fully robust with respect to the dynamic change. Very low computational burden and low needed memory are some advantages of this method. In the first step, with well definition of the state variables, actions, and reward signal, a reinforcement learning problem is formed and then it is solved by Q-learning technique. In the next step the problem is simplified by help of frequency domain modeling information and it is solved by temporal difference method. Finally, a multi-level approach is presented which it can increase the precision without increasing of the memory size. It is shown by simulation that this method works well and the improvement of the multi-level temporal difference method with respect to the Q-learning method is addressed. In this paper a new method based on temporal difference on reinforcement learning algorithm is proposed to active noise control of periodic acoustic signal. This method does not need dynamic information and therefore it is fully robust with respect to the dynamic change. Very low computational burden and low needed memory are some advantages of this method. In the first step, with well definition of the state variables, actions, and reward signal, a reinforcement learning problem is formed and then it is solved by Q-learning technique. In the next step the problem is simplified by help of frequency domain modeling information and it is solved by temporal difference method. Finally, a multi-level approach is presented which it can increase the precision without increasing of the memory size. It is shown by simulation that this method works well and the improvement of the multi-level temporal difference method with respect to the Q-learning method is addressed. https://jsfm.shahroodut.ac.ir/article_174_6ac8bc31cffe676c98b176f6ef923063.pdf