Combining linear filtering and radial basis function networks for accurateprofile recovery

The efficient method presented for the accurate approximation of signal pro files corrupted by noise is based on a principled combination of linear and nonlinear processing. The nonlinear processing is realised using a radial basis network which is designed, trained and validated within the strict ti me constraints set by instrumentation requirements, The quality of profile approximation and the decision to use either linear or nonlinear processing are set by confidence limits which, in turn, are set by the best estimate of current system noise. The approach is described in terms of a novel capi llary electrophoresis instrument with all processing implemented on a dedic ated DSP subsystem.