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arXiv · 1705.00945

Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller

Yu Tsao·Hao-Chun Chu·Shih-Wei Lan·Shih-Hau Fang·Junghsi Lee·Chih-Min Lin
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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.

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Yu Tsao, Hao-Chun Chu, Shih-Wei Lan, Shih-Hau Fang, Junghsi Lee, Chih-Min Lin. 2017-05-02. Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller. https://arxiv.org/abs/1705.00945

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