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This paper proposes a deep cerebellar model articulation controller (DCMAC)for adaptive noise cancellation (ANC). We expand upon the conventional CMAC bystacking sin-gle-layer CMAC models into multiple layers to form a DCMAC modeland derive a modified backpropagation training algorithm to learn the DCMACparameters. Com-pared with conventional CMAC, the DCMAC can characterizenonlinear transformations more effectively because of its deep structure.Experimental results confirm that the pro-posed DCMAC model outperforms theCMAC in terms of residual noise in an ANC task, showing that DCMAC providesenhanced modeling capability based on channel characteristics.

Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller