Adaptive variable step-size neural controller for nonlinear feedback active noise control systems

Abstract

General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms Keywords-Active noise control, filtered-x least-mean-square (FxLMS) method, variable step-size learning, neural network, nonlinear path.

A survey on active noise control in the past decade–Part II: Nonlinear systems

“…From 1997 to 2008, many papers discussed the usage of radial basis function (RBF) networks, recurrent neural networks (RNNs), and fuzzy neural networks for NLANC (see Table 1). In the last decade, some attempts were made by using an ANN as the controller [26,27,28]. A neural controller equipped with the filtered-u least mean square (FuLMS) algorithm with correction terms momentum was proposed in [29].…”

An efficient algorithm for nonlinear active noise control of impulsive noise

“… is the step-size of VFxMCC algorithm which can seriously affect the weight updating rule[29]. What is more, the filter weight vector W(n) of SOV filter is combined by two Therefore, we can choose different  for the updating rule of each filter weight vector independently.…”