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Minimal resource allocation network for adaptive noise cancellation

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3 Author(s)
Yonghong, S. ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore ; Saratchandran, P. ; Sundararajan, N.

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 which 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

Published in:

Electronics Letters  (Volume:35 ,  Issue: 9 )