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This paper presents a novel Wiener-type recurrent neural network with the observer/ Kalman filter identification (OKID) algorithm for unknown dynamic nonlinear system identification. The proposed Wiener-type recurrent network resembles the conventional Wiener model that consists of a dynamic linear subsystem cascaded with a static nonlinear subsystem. The novelties of our approach include: (1) the realization of a conventional Wiener model into a simple connectionist recurrent network whose output can be expressed by a nonlinear transformation of a linear state-space equation; (2) the overall network structure can be determined by the OKID algorithm effectively using only the input-output measurements; and (3) the proposed network is capable of accurately identifying nonlinear dynamic systems using fewer parameters. Computer simulations and comparisons with some existing recurrent networks and learning algorithms have successfully confirmed the effectiveness and superiority of the proposed Wienertype network with the OKID algorithm.

Keywords: Wiener models, recurrent neural networks, observer/Kalman filter identification, minimal state-space model realization, dynamic system identification

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Received April 3, 2007; revised August 10 & October 12, 2007; accepted October 25, 2007.
Communicated by Chin-Teng Lin.