Nadine-a feedforward neural network for arbitrary nonlinear time series

It is shown that Madaline networks applied to the modeling of time series realize a constrained Volterra series because of the fixed nature of the nonlinearity. The authors introduce a novel feedforward structure, named Nadine, that can model arbitrary Volterra series and hence arbitrary, analytic nonlinearities with memory. Nadine can be realized using layers of adaptive linear combiners in which the outputs of one layer are used as the weights rather than the activities of the next layer. This structure admits local adaptation of the linear combiners, making it possible to implement backpropagation-style learning without actually propagating adaptation information between the layers. Nadine is therefore very modular, easy to implement, and readily extendible. High-order neural networks and polynomial discriminant-based methods are the special cases of Nadine which can now be implemented modularly without involving preprocessing