Title :
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
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;
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
DOI :
10.1109/IJCNN.1991.155422
