Nonparametric graded data processing with back-error propagation networksNonparametric graded data processing with back-error propagation networks Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-021993enWe present some computational characteristics of back-error propagation (BEP) networks in processing graded patterns that are otherwise indistinguishable in binary (or bipolar) representations. We address the problem of mapping l-of-m unit gradients with interunit activation profile d to n classes, where maximum noise amplitude of ϵ is permitted within gradient classes. Relations between these parameters and the training period measured in epochs (T) are discussed. Extensions of basic concepts are used to extract embedded feature information in constrained 2D grey-scale patterns and to group and classify distorted pattern clusters whose intraset distances are sometimes greater than interset metrics. Results have been applied to simulation studies in the domain of robotic tactile pattern recognition.http://creativecommons.org/licenses/by/4.0/Information Sciences671-2167-188 Thint Marcus P. Wang Paul P. Dollas Apostolos Δολλας Αποστολος Elsevier Back-error propagation networks