Title :
An LMS algorithm for training single layer globally recursive neural networks
Author :
Stubberud, Peter ; Bruce, J.W.
Author_Institution :
Dept. of Electr. & Comput. Eng., Nevada Univ., Las Vegas, NV, USA
Abstract :
Unlike feedforward neural networks which can act as universal function approximators, recursive neural networks have the potential to act as both universal function approximators and universal system approximators. In this paper, a globally recursive neural network least mean square gradient descent or a real time recursive backpropagation algorithm is developed for a single layer globally recursive neural network that has multiple delays in its feedback path
Keywords :
backpropagation; feedback; function approximation; least mean squares methods; matrix algebra; real-time systems; recurrent neural nets; backpropagation; delays; feedback; function approximators; globally recursive neural network; gradient descent; least mean squares; real time systems; recurrent neural networks; system approximators; Backpropagation; Cost function; Feedforward neural networks; Finite impulse response filter; IIR filters; Least squares approximation; Neural networks; Neurofeedback; Neurons; Recurrent neural networks;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.687204
