On nonlinear modular neural filters

An assessment of the performance of the pipelined recurrent neural network (PRNN) is provided from two aspects, a quantitative one based on the prediction gain and a qualitative one based on examining the changes in the nature of the processed signal. This is achieved by means of the recently introduced ´delay vector variance´ (DVV) method for phase space signal characterisation. A comprehensive analysis of this approach on both linear and nonlinear benchmark signals suggests that the PRNN not only outperforms a single recurrent neural network (RNN) in terms of the prediction gain, but also has better or similar performance in terms of preserving the nature of the processed signal.