A structure by which a recurrent neural network can approximate a nonlinear dynamic system

Author

Seidl, David R. ; Lorenz, Robert D.

Author_Institution

Dept. of Electr. & Comput. Eng., Wisconsin Univ., Madison, WI, USA

fYear

1991

fDate

8-14 Jul 1991

Firstpage

709

Abstract

It is shown that the structure of the standard recurrent neural network has the capacity to model a broad class of nonlinear dynamic systems. The key result is that the structure of the recurrent neural network permits the internal formation of a single hidden layer/linear output layer feedforward neural network to approximate the next system state as a function of the current system state and the inputs. The recurrent nature of the network allows the single weight matrix to serve as both the input and output weight matrices of the internal feedforward network

Keywords

matrix algebra; neural nets; nonlinear control systems; internal feedforward network; nonlinear dynamic system; recurrent neural network; single hidden layer/linear output layer feedforward neural network; system state approximation; weight matrix; Computer networks; Feedforward neural networks; Linear approximation; Multi-layer neural network; Neural networks; Nonlinear dynamical systems; Nonlinear systems; Recurrent neural networks; Standards development; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on

Conference_Location

Seattle, WA

Print_ISBN

0-7803-0164-1

Type

conf

DOI

10.1109/IJCNN.1991.155422

Filename

155422