Abstract
An investigation into the performance of the recently developed minimal resource allocation network (MRAN) for adaptive noise cancellation problems is presented and a comparison made with the recurrent radial basis function (RBF) network of Billings and Fung. An MRAN has the same structure as an RBF network but uses a sequential learning algorithm that adds and prunes hidden neurons as input data are received sequentially to produce a compact network. Simulation results for nonlinear noise cancellation examples show that an MRAN, with a much smaller network, produces better noise reduction than the recurrent RBF.
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