  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.
