On realizability of neural networks-based input-output models in the classical state-space form

Kotta U; Chowdhury FN; Nomm S

Automatica, Vol.42, No.7, 1211-1216, 2006

DOI10.1016/j.automatica.2006.03.003 Export Citation

On realizability of neural networks-based input-output models in the classical state-space form

This paper proves that the typical neural network-based input/output model does not have a state-space realization and suggests the Additive Nonlinear Auto-Regressive with eXogenous input (ANARX) structure as an excellent choice for neural-network-based input-output models. The advantage of the ANARX model is that the time-steps in the argument are pair-wise decomposed, which allows the ANARX model to be realized in state space, and to be linearized via dynamic output feedback. Moreover, accessibility of the state-space realization has been proved. (c) 2006 Elsevier Ltd. All rights reserved.

Keywords:discrete-time nonlinear systems;input-output models;neural networks;realizability