Title :
Bilinear recurrent neural network
Author :
Park, Dong C. ; Zhu, Yan
Author_Institution :
Dept. of Electr. & Comput. Eng., Florida Int. Univ., Miami, FL, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
A recurrent neural network and its training algorithm are proposed in this paper. Since the proposed algorithm is based on the bilinear polynomial, it can model many nonlinear systems with much more parsimony than the higher order neural networks based on Volterra series. The proposed bilinear recurrent neural network (BLRNN) is compared with multilayer perceptron neural networks (MLPNN) for time series prediction problems. The results show that the BLRNN is robust and outperforms the MLPNN in terms of prediction accuracy
Keywords :
learning (artificial intelligence); multilayer perceptrons; prediction theory; recurrent neural nets; time series; bilinear polynomial; bilinear recurrent neural network; multilayer perceptron neural networks; nonlinear systems; prediction accuracy; time series prediction problems; training algorithm; Artificial neural networks; Computer networks; Electronic mail; Feedforward neural networks; Intelligent networks; Neural networks; Neurons; Nonlinear systems; Polynomials; Recurrent neural networks;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374501
