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Data Driven Model Estimation: A System Identification Package for Predicting Time-Series and to Enhance Dynamical Models from Data
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| Organization: | Chalmers University of Technology |
| Organization: | Royal Institute of Technology |
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2006 Wolfram Technology Conference
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Champaign IL
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This talk describes a system identification package that is under development. Previews of algorithms and supported model structures are given in connection with examples. The package supports algorithms for identification of linear and nonlinear models of dynamical systems that can be used for simulation, prediction, and for control design. The symbolic features of Mathematica are used so that a large variety of model structures can be handled. Before the numerical estimation procedure, the symbolic definition of the model structure is simplified and modified before the numerical computations of the parameters. In this way a larger set of model structures can be supported, at the same time as the computational speed is improved.
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time series, model estimation, data
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| DataDrivenModelEstimation.nb (3.3 MB) - Mathematica Notebook [for Mathematica 5.2] |
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