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2 - nonlinear system identification via recurrent neural networks. (English) Zbl 1209.93035
Summary: This paper proposes an 2 - identification scheme as a new robust identification method for nonlinear systems via recurrent neural networks. Based on linear matrix inequality (LMI) formulation, for the first time, the 2 - learning algorithm is presented to reduce the effect of disturbance to an 2 - induced norm constraint. New stability results, such as boundedness, input-to-state stability (ISS), and convergence, are established in some senses. It is shown that the design of the 2 - identification method can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. A numerical example is presented to demonstrate the validity of the proposed identification scheme.
MSC:
93B30System identification
93B36H -control
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