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.

[1]  Honglak Lee,et al.  Sparse deep belief net model for visual area V2 , 2007, NIPS.

[2]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition , 2012 .

[3]  R. Srikant,et al.  Why Deep Neural Networks? , 2016, ArXiv.

[4]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[5]  F. Daum.  Nonlinear filters: beyond the Kalman filter , 2005, IEEE Aerospace and Electronic Systems Magazine.

[6]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[7]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[8]  Rich Caruana,et al.  Do Deep Nets Really Need to be Deep? , 2013, NIPS.

[9]  Frank L. Lewis,et al.  CMAC neural networks for control of nonlinear dynamical systems: Structure, stability and passivity , 1997, Autom..

[10]  Li Tan,et al.  Adaptive Volterra filters for active control of nonlinear noise processes , 2001, IEEE Trans. Signal Process..

[11]  Chih-Min Lin,et al.  Adaptive CMAC-based supervisory control for uncertain nonlinear systems , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Zong-Ru Yu,et al.  Application of CMAC and FPGA to a twin rotor MIMO system , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[13]  Chin-Pao Hung.  Integral variable structure control of nonlinear system using a CMAC neural network learning approach , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Chin-Hui Lee,et al.  An artificial neural network approach to automatic speech processing , 2014, Neurocomputing.

[15]  Yanjun Qi,et al.  Unsupervised Feature Learning by Deep Sparse Coding , 2013, SDM.

[16]  Jui-Yu Wu.  MIMO CMAC neural network classifier for solving classification problems , 2011, Appl. Soft Comput..

[17]  Daniel S. Yeung,et al.  Adaptive Filter Design Using Recurrent Cerebellar Model Articulation Controller , 2010, IEEE Transactions on Neural Networks.

[18]  Chin-Teng Lin,et al.  An adaptive neural fuzzy filter and its applications , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[19]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[21]  A. Cichocki,et al.  Multilayer nonnegative matrix factorisation , 2006 .

[22]  Frank L. Lewis,et al.  Optimal design of CMAC neural-network controller for robot manipulators , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[23]  Chih-Min Lin,et al.  RCMAC Hybrid Control for MIMO Uncertain Nonlinear Systems Using Sliding-Mode Technology , 2007, IEEE Transactions on Neural Networks.

[24]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[25]  B. Widrow,et al.  Adaptive noise cancelling: Principles and applications , 1975 .

[26]  G. Horvath,et al.  CMAC neural network with improved generalization property for system modeling , 2002, IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276).

[27]  R. Srikant,et al.  Why Deep Neural Networks for Function Approximation? , 2016, ICLR.

[28]  V. John Mathews.  Adaptive Volterra filters using orthogonal structures , 1996, IEEE Signal Processing Letters.

[29]  M.G. Simoes,et al.  Parametric CMAC networks: fundamentals and applications of a fast convergence neural structure , 2002, Conference Record of the 2002 IEEE Industry Applications Conference. 37th IAS Annual Meeting (Cat. No.02CH37344).