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 language | Unknown |
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Title of host publication | IEEE |
Pages | 2053 - 2058 |
DOIs | |
Publication status | Published - 1 Jan 2009 |
Event | IEEE International Symposium on Industrial Electronics (ISIE) - Duration: 1 Jan 2009 → … |
Conference
Conference | IEEE International Symposium on Industrial Electronics (ISIE) |
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Period | 1/01/09 → … |