A Comparison of Discrete-Time Operator Models for Nonlinear System Identification

Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)

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Andrew Back, Ah Tsoi


We present a unifying view of discrete-time operator models used in the context of finite word length linear signal processing. Comparisons are made between the recently presented gamma operator model, and the delta and rho operator models for performing nonlinear system identification and prediction using neural networks. A new model based on an adaptive bilinear transformation which generalizes all of the above models is presented.