Adaptive Volterra filters for active control of nonlinear noise processes

By Li Tan, Jean Jiang. IEEE Transactions on Signal Processing, 2001

369 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/906424e2cbac753413fa7d8a18e753c3000b506d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Kunal Kumar Das
2. Jitendriya Kumar Satapathy

In Computational Mechanics

1. Sen Maw Kuo
2. Woon-Seng Gan
3. Kunal Kumar Das
4. Jitendriya Kumar Satapathy
5. Yoshinobu Kajikawa

In Signal Processing

1. Sen Maw Kuo
2. Woon-Seng Gan
3. Kunal Kumar Das
4. Jitendriya Kumar Satapathy
5. Yoshinobu Kajikawa

Citations in the corpus

• Recent advances on active noise control: open issues and innovative applications (2012, background): “nonlinear filters, such as multilayer artificial neural networks [205–209], truncated Volterra expansions [210–215], radial basis functions [210], functional link artificial neural networks [216–224], adaptive bilinear filters [225], general function expansion nonlinear filters [226], and polynomial”
• A reduced complexity adaptive legendre neural network for nonlinear active noise control (2012, background): “Index Terms— Nonlinear active noise control, Legendre neural network, Filtered-l LMS algorithm, Partial updates.”
• Legendre Neural Network for nonlinear Active Noise Cancellation with nonlinear secondary path (2011, methodology): “The major advantage of LNN over FLANN is that the evaluation of Legendre polynomials involves less computation compared to that of the trigonometric functions.”