Estimating the Number of Hidden Neurons in Recurrent Neural Networks for Nonlinear System Identification

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

The problem of complexity is here addressed by defining an upper bound for the number of the hidden layer's neurons. This majorant is evaluated by applying a singular value decomposition to the contaminated oblique subspace projection of the row space of future outputs into the past inputs-outputs row space, along the future inputs row space. Full rank projections are dealt with by i) computing the number of dominant singular values, on the basis of a threshold related to the Euclidean norm of an artificial error matrix and ii) finding the argument of minimizing the singular value criterion. Results on a benchmark three-tank system demonstrate the effectiveness of the proposed methodology.
Original languageUnknown
Title of host publicationIEEE
Pages2053 - 2058
DOIs
Publication statusPublished - 1 Jan 2009
EventIEEE International Symposium on Industrial Electronics (ISIE) -
Duration: 1 Jan 2009 → …

Conference

ConferenceIEEE International Symposium on Industrial Electronics (ISIE)
Period1/01/09 → …

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