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Nonlinear identification with local model networks using GTLS techniques and equality constraints.
Christoph Hametner1, Stefan Jakubek
1Institute of Mechanics and Mechatronics, Division of Control and Process Automation, Vienna University of Technology, Vienna, Austria. hametner@impa.tuwien.ac.at
IEEE Transactions on Neural Networks
|July 27, 2011
Summary
This study introduces a method to improve nonlinear system identification by integrating quantitative process knowledge into local model networks. This enhances accuracy in complex system modeling using constrained estimation techniques.
Area of Science:
- Engineering
- Systems Science
- Control Theory
Background:
- Local model networks approximate nonlinear systems using multiple local models within a partitioned space.
- Integrating structured knowledge simplifies the identification of complex nonlinear processes.
Purpose of the Study:
- To extend local model networks by incorporating quantitative process knowledge into the identification procedure.
- To enhance nonlinear system identification through explicit input-output dependencies and equality constraints.
Main Methods:
- Integration of quantitative knowledge via equality constraints in parameter estimation.
- Development of a constrained generalized total least squares algorithm for local parameter estimation.
- Combination of expectation-maximization with constrained parameter estimation for partitioning.
Main Results:
- Demonstrated benefits and applicability of the proposed concepts through illustrative examples.
- Successful application using real measurement data, validating the enhanced identification approach.
- Improved accuracy in nonlinear system modeling via constrained local model networks.
Conclusions:
- The proposed method effectively integrates quantitative process knowledge into local model networks.
- Constrained estimation and partitioning strategies enhance the identification of complex nonlinear systems.
- The approach offers a robust framework for real-world applications in system identification.
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