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» On computational limitations of neural network architectures
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ICPR
2004
IEEE
14 years 6 months ago
Improvement of Bidirectional Recurrent Neural Network for Learning Long-Term Dependencies
Bidirectional recurrent neural network(BRNN) is a noncausal generalization of recurrent neural network(RNN). It can not learn remote information efficiently due to the problem of ...
Jinmiao Chen, Narendra S. Chaudhari
ETD2000
1995
13 years 9 months ago
Automatic generation of a neural network architecture using evolutionary computation
This paper reports the application of evolutionary computation in the automatic generation of a neural network architecture.It is a usual practice to use trial and error to find a...
E. Vonk, Lakhmi C. Jain, L. P. J. Veelenturf, R. J...
ACSC
2008
IEEE
13 years 7 months ago
An investigation of the state formation and transition limitations for prediction problems in recurrent neural networks
Recurrent neural networks are able to store information about previous as well as current inputs. This "memory" allows them to solve temporal problems such as language r...
Angel Kennedy, Cara MacNish
FLAIRS
2006
13 years 7 months ago
GFAM: Evolving Fuzzy ARTMAP Neural Networks
Fuzzy ARTMAP (FAM) is one of the best neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in...
Ahmad Al-Daraiseh, Michael Georgiopoulos, Annie S....