Estimating the number of hidden neurons in recurrent neural networks for nonlinear system identification
Paulo Sousa Gil, Alberto Cardoso, Luís Brito Palma · 2009
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.