Continuous time NLq theory: absolute stability criteria

Author

Suykens, J.A.K. ; Vandewalle, J.

Author_Institution

ESAT, Katholieke Univ., Leuven, Heverlee, Belgium

Volume

3

fYear

1999

fDate

1999

Firstpage

1481

Abstract

We present absolute stability (global asymptotic) criteria for continuous time multilayer recurrent neural networks with two hidden layers. Such forms arise when considering recurrent neural models and neural controllers for a given plant, both parametrized by multilayer perceptrons with one-hidden layer. The one-hidden layer case corresponds to systems in Lur´e form. These results are related to the NLq theory which is a stability theory for q-layered discrete time multilayer recurrent neural networks with conditions for global asymptotic stability and input-output stability with finite L₂-gain. The criteria can be used to constrain dynamic backpropagation in order to impose closed-loop stability for neural control schemes

Keywords

absolute stability; asymptotic stability; backpropagation; closed loop systems; feedforward neural nets; multilayer perceptrons; neurocontrollers; recurrent neural nets; Lure form; NLq theory; absolute stability; asymptotic stability; backpropagation; closed-loop systems; multilayer neural networks; multilayer perceptrons; neurocontrollers; recurrent neural networks; Asymptotic stability; Backpropagation; Constraint theory; Linear feedback control systems; Linear matrix inequalities; Multi-layer neural network; Nonhomogeneous media; Nonlinear dynamical systems; Recurrent neural networks; Stability criteria;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.832587

Filename

832587