Mixed Order Hyper-Networks for Function Approximation and Optimisation
K Swingler - 2016 - dspace.stir.ac.uk
Many systems take inputs, which can be measured and sometimes controlled, and outputs,
which can also be measured and which depend on the inputs. Taking numerous …
which can also be measured and which depend on the inputs. Taking numerous …
A comparison of learning rules for mixed order hyper networks
K Swingler - 2015 7th International Joint Conference on …, 2015 - ieeexplore.ieee.org
A mixed order hyper network (MOHN) is a neural network in which weights can connect any
number of neurons, rather than the usual two. MOHNs can be used as content addressable …
number of neurons, rather than the usual two. MOHNs can be used as content addressable …
Structure discovery in mixed order hyper networks
K Swingler - Big Data Analytics, 2016 - Springer
Abstract Background Mixed Order Hyper Networks (MOHNs) are a type of neural network in
which the interactions between inputs are modelled explicitly by weights that can connect …
which the interactions between inputs are modelled explicitly by weights that can connect …