A modified probabilistic neural network (PNN) for nonlinear time series analysis

A modified PNN (probabilistic neural network) is proposed that can be used for nonlinear time series analysis without loss of the advantages offered by D.F. Specht´s PNN architecture (1988, 1990). It is shown how the Gaussian radial basis function, expressed as a Parzen probability density function estimator, can be used to estimate and implement nonlinear mappings, applied to time series data. The performance of this modified PNN is demonstrated by showing its effectiveness in smoothing a sinusoidal signal which has been compressed in amplitude and then corrupted with wideband non-Gaussian noise. The network is also compared with the multipass learning backpropagation network and the relative merits of the proposed modified PNN are discussed