Complexity reduction in Volterra connectionist networks using a self-structuring LMS algorithm

This paper describes the development of an algorithm for structure optimisation in linear weight neural networks which although maintaining a unimodal error surface adaptively optimises network structure. The methods developed may be applied to any network which is linear in its weights, for example the radial basis function (RBF) networks and Volterra networks. These linear weight networks (LWNs) are important as their error surfaces are unimodal allowing high speed single run learning. By use of the optimal output mapper they may also be shown to have lighter computational loads in general than hidden layer back propagation (HLBP) networks