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CEC
2005
IEEE
15 years 9 months ago
Evolving improved incremental learning schemes for neural network systems
It is well known that incremental learning can often be difficult for traditional neural network systems, due to newly learned information interfering with previously learned infor...
Tebogo Seipone, John A. Bullinaria
APPINF
2003
15 years 4 months ago
Preventing Computational Chaos in Asynchronous Neural Networks
One of the primary advantages of artificial neural networks is their inherent ability to perform massively parallel, nonlinear signal processing. However, the asynchronous dynamics...
Jacob Barhen, Vladimir Protopopescu
NIPS
2003
15 years 4 months ago
Training a Quantum Neural Network
Most proposals for quantum neural networks have skipped over the problem of how to train the networks. The mechanics of quantum computing are different enough from classical compu...
Bob Ricks, Dan Ventura
ICONIP
1998
15 years 4 months ago
Statistical Methods for Construction of Neural Networks
Despite all the progress in neural networks the technology is still brittle and sometimes difficult to apply. Automatic construction of networks and proper initialization of their...
Wlodzislaw Duch, Rafal Adamczak
IJON
2006
138views more  IJON 2006»
15 years 3 months ago
Time-series prediction using a local linear wavelet neural network
A local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weigh...
Yuehui Chen, Bo Yang, Jiwen Dong