Removal of degeneracy in adaptive Volterra networks by dynamic structuring

The authors describe the development of a new form of connectionist model or neural network which, although having a unimodal error surface, will optimize its structure adaptively. The network is derived from the Volterra connectionist model. The adaptive noninformative extension nonlinearities removal method is used. The method requires only a slight computational expenditure in order to perform the self-structuring operation and may be operated with a bias to network contraction or reduction of error. Also, it does not require exact redundancies in order to operate. In addition, the inherent null-space nonlinearities removal method demonstrates a much more fundamental basis for removal of redundancy. This approach provides a deeper understanding of the problem and may in its current form be used as a block method with training set data.<>