Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller

This paper proposes a deep cerebellar model articulation controller (DCMAC) for adaptive noise cancellation (ANC). We expand upon the conventional CMAC by stacking single-layer CMAC models into multiple layers to form a DCMAC model, and derive a backpropagation training algorithm to learn the DCMAC parameters. Compared with the conventional CMAC, the DCMAC can characterize nonlinear transformations more effectively because of its deep structure. Experimental results confirm that the proposed DCMAC model outperforms the CMAC in terms of residual noise in an ANC task, showing that the DCMAC provides enhanced capability to model channel characteristics.