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- W577125315 abstract "In this master thesis, an extension of the interaction matrix formulation to the discrete-time bilinear state-space model is derived. Several identi cation techniques are presented from this formulation to identify the bilinear state-space matrices using a superstate vector, derived from a single set of su ciently rich input-output measurements. The initial state can be non-zero and unknown. Unlike other approaches, no specialized inputs are required, such as sinusoidal or white inputs, or duration-varying unit pulses involving multiple experiments. For that reason, the bilinear state-space identi cation problem is di cult to solve, since it can be seen as a linear time-varying system with input-dependent system matrix or state-dependent input-in uence matrix. The resultant input-output map from this state-space formulation can be used for output prediction. A relationship between the coe cients of this input-output map and the bilinear state-space model matrices is obtained via two interaction matrices, corresponding to the linear and bilinear portions of the model, respectively. Numerical examples are provided to illustrate these bilinear state-space model identi cation techniques and the input-output model identi cation method. It is concluded that the proposed identi cation algorithms can correctly identify the original bilinear state-space model, and the identi ed input-output map correctly predict its system output response, despite the fact that the interaction matrices are only implicitly assumed to exist." @default.
- W577125315 created "2016-06-24" @default.
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- W577125315 date "2010-01-01" @default.
- W577125315 modified "2023-09-27" @default.
- W577125315 title "Identi cation and Prediction of Discrete-Time Bilinear State-Space Models: Interaction Matrices and Superstates" @default.
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