Active Noise Control Systems: Algorithms and DSP Implementations
Active Noise Control Systems: Algorithms and DSP Implementations
Model-Free control of a nonlinear ANC system with a SPSA-Based neural network controller
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Active control of nonlinear noise processes in a linear duct
IEEE Transactions on Signal Processing
Adaptive Volterra filters for active control of nonlinear noiseprocesses
IEEE Transactions on Signal Processing
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
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In practical active noise control (ANC) systems, the primary path and the secondary path may be nonlinear. The actuators of an ANC system often have non-minimum phase response. A linear controller under such situations yields poor performance. Neural networks using Filtered-x back-propagation (FX-BP) algorithm are often used as a controller for the nonlinear ANC systems. But FX-BP algorithm often converges slowly and may converge to a local minimum. A novel feedforward network-based ANC algorithm is proposed in this paper. The Online Sequential Extreme Learning Machine(OS-ELM) is generalized to meet the requirements of the nonlinear ANC systems. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the FX-BP algorithm when the primary path is nonlinear.