Original ContributionA comparison study of binary feedforward neural networks and digital circuits
References (16)
Neural computing architectures
(1988)- et al.
Some results concerning adaptive logic networks
- et al.
Numerical study of phase transitions in Potts models
Physiological Review
(1991) - et al.
The patch algorithm: Fast design of binary feedforward neural networks
Neural Networks
(1992) Digital networks and computer systems
(1971)Threshold logic: A synthesis approach
(1965)The upstart algorithm: A method for constructing and training feedforward neural networks
Neural Computation
(1990)- et al.
A fast partitioning algorithm and a comparison of binary feedforward neural networks
Europhysics Letters
(1992)
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C-Mantec: A novel constructive neural network algorithm incorporating competition between neurons
2012, Neural NetworksCitation Excerpt :Choosing the proper neural network architecture for a given classification problem remains a difficult issue (Baum & Haussler, 1989; Gómez, Franco, & Jerez, 2009; Lawrence, Giles, & Tsoi, 1996; Rumelhart, Hinton, & Williams, 1986), and despite the existence of several proposals to solve or alleviate this problem (Haykin, 1994), there is no general agreement on the strategy to follow in order to select an optimal neural network architecture. The computationally inefficient “trial and error” method is still much used in applications using Artificial Neural Networks (ANNs), but as an alternative different neural constructive algorithms have been proposed in recent years (Andree, Barkema, Lourens, Taal, & Vermeulen, 1993; Fahlman & Lebiere, 1990; Frean, 1990; García-Pedrajas & Ortiz-Boyer, 2007; Keibek, Barkema, Andree, Savenlie, & Taal, 1992; Mezard & Nadal, 1989; Nicoletti & Bertini, 2007; Parekh, Yang, & Honavar, 2000; Subirats, Jerez, & Franco, 2008; Utgoff & Stracuzzi, 2002). In general, constructive methods start with a small network (normally a single neuron in a single hidden layer) to then add new units as needed until a stopping criteria is met.
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