ANN modeling of Volterra systems
Garry W. Davis, Michael L. Gasperi · 2002
The authors describe ANN (artificial neural network) simulation experiments that were performed to try to gauge the abilities of ANNs to model systems possessing strong, higher-order nonlinearities. The target nonlinear system was always a Volterra system. The user could specify the degree of nonlinearity by selecting which Volterra terms to include in the series. The ANN architecture was either a feedforward or a recurrent architecture. The number of processing elements and connectivity were varied from experiment to experiment in an effort to establish a relation between Volterra series order and the ANN architecture which succeeded best in modeling the Volterra system. Two typical examples are discussed in detail. Current results indicate that a recurrent network architecture is comparatively more efficient in modeling specific Volterra systems.>