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

Authors

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.

Subject

Neural Networks

Conference

IEEE CIS-RAM, June 2006