2,159 research outputs found

    Active Control of Sound based on Diagonal Recurrent Neural Network

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    Active Control of Sound based on Diagonal Recurrent Neural Network

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    Review of active noise control techniques with emphasis on sound quality enhancement

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    The traditional active noise control design aims to attenuate the energy of residual noise, which is indiscriminative in the frequency domain. However, it is necessary to retain residual noise with a specified spectrum to satisfy the requirements of human perception in some applications. In this paper, the evolution of active noise control and sound quality are briefly discussed. This paper emphasizes on the advancement of active noise control method in the past decades in terms of enhancing the sound quality

    Weighted Robust Adaptive Filtering in Krein Space and Its Application in Active Noise Control

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    Weighted Robust Adaptive Filtering in Krein Space and Its Application in Active Noise Control

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    A Model Predictive Algorithm for Active Control of Nonlinear Noise Processes

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    Neuro-fuzzy identification of an internal combustion engine

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    Dynamic modeling and identification of an internal combustion engine (ICE) model is presented in this paper. Initially, an analytical model of an internal combustion engine simulated within SIMULINK environment is excited by pseudorandom binary sequence (PRBS) input. This random signals input is chosen to excite the dynamic behavior of the system over a large range of frequencies. The input and output data obtained from the simulation of the analytical model is used for the identification of the system. Next, a parametric modeling of the internal combustion engine using recursive least squares (RLS) technique within an auto-regressive external input (ARX) model structure and a nonparametric modeling using neuro-fuzzy modeling (ANFIS) approach are introduced. Both parametric and nonparametric models verified using one-step-ahead (OSA) prediction, mean squares error (MSE) between actual and predicted output and correlation tests. Although both methods are capable to represent the dynamic of the system very well, it is demonstrated that ANFIS gives better prediction results than RLS in terms of mean squares error achieved between the actual and predicted signals
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