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.

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