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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 signaldoi:10.1109/tnn.2007.902728 pmid:18269949 fatcat:2ennrs72vvbt3ap5dngnk2q5oy