Abstract
A framework for the assessment of qualitative performance of machine learning architectures is proposed. For generality, the analysis is provided for the modular nonlinear pipelined recurrent neural network (PRNN) architecture. This is supported by a sensitivity analysis, which is achieved based upon the prediction performance with respect to changes in the nature of the processed signal and by utilizing the recently introduced delay vector variance (DVV) method for phase space signal characterization. Comprehensive simulations combining the quantitative and qualitative analysis on both linear and nonlinear signals suggest that better quantitative prediction performance may need to be traded in order to preserve the nature of the processed signal, especially where the signal nature is of primary importance (biomedical applications).
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