Minimal resource allocation network for adaptive noise cancellation

An investigation into the performance of the recently developed minimal res ource 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 R BF network but uses a sequential learning algorithm that adds and prunes hi dden neurons as input data are received sequentially to produce a compact n etwork. Simulation results for nonlinear noise cancellation examples show t hat an MRAN, with a much smaller network, produces better noise reduction t han the recurrent RBF.