Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller
Systems and Control
2017-05-03 v1
Abstract
This paper proposes a deep cerebellar model articulation controller (DCMAC) for adaptive noise cancellation (ANC). We expand upon the conventional CMAC by stacking sin-gle-layer CMAC models into multiple layers to form a DCMAC model and derive a modified backpropagation training algorithm to learn the DCMAC parameters. Com-pared with conventional CMAC, the DCMAC can characterize nonlinear transformations more effectively because of its deep structure. Experimental results confirm that the pro-posed DCMAC model outperforms the CMAC in terms of residual noise in an ANC task, showing that DCMAC provides enhanced modeling capability based on channel characteristics.
Keywords
Cite
@article{arxiv.1705.00945,
title = {Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller},
author = {Yu Tsao and Hao-Chun Chu and Shih-Wei Lan and Shih-Hau Fang and Junghsi Lee and Chih-Min Lin},
journal= {arXiv preprint arXiv:1705.00945},
year = {2017}
}