The dynamics and training of recurrent nets which make use of probabilistic nodes based on Boolean functions are explored. These are shown to be equivalent ...
These are functionally equivalent to higher order nodes or sigma-pi units but have the potential to be implemented in readily available memory components. The ...
The dynamics and training of recurrent nets which make use of probabilistic nodes based on Boolean functions are explored. These are shown to be equivalent ...
Feb 5, 1992 · The novelty of this paper is that it utilises the associative nature of the Sigma-pi neuron model and their bounded quantised site-values ...
Missing: Recurrent | Show results with:Recurrent
This paper investigates the nature of training in two types of net. One uses a stochastic variant of the semilinear Threshold Logic Unit (TLU), while the other ...
The dynamics and training of recurrent nets which make use of probabilistic nodes based on Boolean functions are explored. These are shown to be equivalent to ...
Training Recurrent Nets of Hardware Realisable Sigma-PI Units. Article. Jan 1992. Kevin Gurney. The dynamics and training of recurrent nets which ...
Product unit neural networks are useful because they can handle higher order combinations of inputs. When trained using traditional backpropagation, ...
Missing: Realisable | Show results with:Realisable
Neural Networks 6, 133–45. Gurney, K.N. 1992c. Training recurrent nets of hardware realisable sigma-pi units. International Journal of. Neural Systems 3, 31 ...
A new training rule was introduced - the delta rule - which, it was claimed, could be generalised from the single unit/layer case to multilayer nets. This is ...