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