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Transactions of the Institute of Measurement and Control
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A Novel Linear Recurrent Neural Network for Multivariable System Identification

Minrui Fei

School of Mechatronical Engineering and Automation, Shanghai University Shanghai 200072, China, mrfei888{at}x263.net

Jian Zhang

School of Mechatronical Engineering and Automation, Shanghai University Shanghai 200072, China

Huosheng Hu

Department of Computer Science, University of Essex, Colchester CO4 3SQ, UK

Taicheng Yang

Department of Engineering, University of Sussex, Brighton BN1 9QT, UK

This paper proposes a novel linear recurrent neural network for multivariable system identification, namely a linerec neural network (LNN). Based on this network, the transfer function matrix model of a multivariable system can be identified directly according to its input and output data. In this way, LNNs differ from existing neural networks. An LNN is constructed based on the identification of prior knowledge in a system, and its weights have definite physical meaning. An LNN is equivalent to a linear equation set, and its training algorithm is based on Widrow-Hoff learning rules. In this paper, the theoretical foundation, structural algorithm and learning rules of LNNs are proposed and studied. To guarantee learning convergence, network training stability is analysed using discrete Lyapunov stability theory. Finally, simulation results show the feasibility of LNNs for multivariable system identification.

Key Words: discrete Lyapunov function • linear recurrent neural network • multivariable system identification • piecewise linear function

Transactions of the Institute of Measurement and Control, Vol. 28, No. 3, 229-242 (2006)
DOI: 10.1191/0142331206tim171oa
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