NDLI: Bilinear recurrent neural network

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Generalization and fault tolerance in rule-based neural networks

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Bilinear recurrent neural network

Content Provider	IEEE Xplore Digital Library
Author	Park, D.C. Yan Zhu
Copyright Year	1994
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Florida Int. Univ., Miami, FL, USA (Park, D.C.; Yan Zhu)
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.
Starting Page	1459
Ending Page	1464
File Size	301079
Page Count	6
File Format	PDF
ISBN	078031901X
DOI	10.1109/ICNN.1994.374501
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Neurons Polynomials Neural networks Nonlinear systems Artificial neural networks Feedforward neural networks Intelligent networks Computer networks Electronic mail
Content Type	Text
Resource Type	Article

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