Abstract—The Cerebellar Model Articulation Controller
(CMAC) neural network is an associative memory that is
biologically inspired by the cerebellum, which is found in the
brains of animals. The standard CMAC uses the least mean
squares algorithm (LMS) to train the weights. Recently, the
recursive least squares (RLS) algorithm was proposed as a
superior algorithm for training the CMAC online as it can
converge in one epoch, and does not require tuning of a learning
rate. However, the RLS algorithm was found to be very
computationally demanding. In this work, the RLS
computation time is reduced by using an inverse QR
decomposition based RLS (IQR-RLS) algorithm which is also
parallelized for multi-core CPUs. Furthermore, this work
shows how the IQR-RLS algorithm may be regularized which
greatly improves the generalization capabilities of the CMAC.
Index Terms—CMAC, inverse QR-RLS, regularization, recursive least squares.
The authors are with the Department of Electrical and Electronic Engineering, University of Auckland, Auckland, New Zealand (e-mail: email@example.com; firstname.lastname@example.org).
Cite:C. W. Laufer and G. Coghill, "A Regularized Inverse QR Decomposition Based Recursive Least Squares Algorithm for the CMAC Neural Network," International Journal of Machine Learning and Computing vol.2, no. 4, pp. 481-486, 2012.