Using a Wiener-Type Recurrent Neural Network with the Minimum Description Length Principle for Dynamic System Identification

Authors:
Jeen-Shing Wang;Hung-Yi Lin;Yu-Liang Hsu;Ya-Ting Yang
Affiliations:
Department of Electrical Engineering,;Department of Electrical Engineering,;Department of Electrical Engineering,;Institute of Education, National Cheng Kung University, Tainan 701, Taiwan, R.O.C.
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

This paper presents a novel Wiener-type recurrent neural network with the minimum description length (MDL) principle 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 state-space equation mapped from the network topology can be used to analyze the characteristics of the network using the well-developed theory of linear systems; and 3) the overall network structure can be determined by the MDL principle effectively using only the input-output measurements. Computer simulations and comparisons with some existing recurrent networks have successfully confirmed the effectiveness and superiority of the proposed Wiener-type network with the MDL principle.