Model-based neural networks

In this paper we show how neural networks can be formulated in terms of various parameterised connection models which explicitly encode desired properties of the target system. Such a modelling approach to neural networks raises issues about their relationships to other technologies such as Adaptive Filtering and Principal Components Analysis. The benefits of this approach can be a significant decrease in the parameter space, improved generalisation, and a learning procedure which guarantees a priori specified invariance constraints.