On State-Space Neural Networks for Systems Identification: Stability and Complexity

Abstract

The problem of order estimation and global stability in affine three-layered state-space neural networks is here addressed. An upper bound for the number of neurons to be inserted in the hidden layer is computed using a subspace technique. Some sufficient conditions for the global asymptotic stability are presented using the Lyapunov stability theory and the contraction mapping theorem