A comparison of learning rules for Mixed Order Hyper Networks

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 memories with higher capacity than standard Hopfield networks. They can also be used for regression, clustering, classification, and as fitness models for use in heuristic optimisation. This paper presents a set of methods for estimating the values of the weights in a MOHN from training data. The different methods are compared to each other and to a standard MLP trained by back propagation and found to be faster to train than the MLP and more reliable as the error function does not contain local minima.