Adaptive signal processing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Active Noise Control Systems: Algorithms and DSP Implementations
Active Noise Control Systems: Algorithms and DSP Implementations
Leaky delayed LMS algorithm: stochastic analysis for Gaussian data and delay modeling error
IEEE Transactions on Signal Processing
Improved training of neural networks for the nonlinear active control of sound and vibration
IEEE Transactions on Neural Networks
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This paper introduces a novel neural filtered-U recursive least mean square (NFURLMS) algorithm and its corresponding weight updating method to the application of active noise control (ANC) system. Instead of the complex designing procedures, the proposed approach uses few mathematical transfer functions to design the ANC system. The correction terms momentum to avoid the premature saturation of back-propagation algorithm and the way to design the optimal learning rate are also included in the paper to improve the noise reduction performance. In addition, the proposed method protects ANC systems against unstable poles such as occur in conventional filtered-U design. Several simulation results show that the proposed method can effectively cancel the narrowband and broadband noise in an ANC system.