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- W2151878313 abstract "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. >" @default.
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- W2151878313 date "2002-12-09" @default.
- W2151878313 modified "2023-09-23" @default.
- W2151878313 title "ANN modeling of Volterra systems" @default.
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- W2151878313 doi "https://doi.org/10.1109/ijcnn.1991.155425" @default.
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