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Automated nonlinear system modeling with multiple fuzzy neural networks and kernel smoothing
International Journal of Neural Systems
|October 15, 2010
Summary
This study introduces a new fuzzy neural network approach for automated model identification, addressing uncertainties in structure and parameters. It offers an integrated framework for selecting model structures and identifying parameters efficiently.
Area of Science:
- Control Engineering
- Artificial Intelligence
- Computational Intelligence
Background:
- Uncertainties in model structure and parameters are significant challenges in system identification.
- Existing methods often require manual intervention for structure selection and parameter estimation.
- Addressing these uncertainties is crucial for robust control system design.
Purpose of the Study:
- To propose an integrated analytic framework for automated structure selection and parameter identification.
- To develop a novel identification approach using fuzzy neural networks.
- To handle structural changes and ensure desired performance during identification.
Main Methods:
- Utilizing fuzzy neural networks for system identification.
- Employing a kernel smoothing technique for automatic model structure generation within a fixed time interval.
- Implementing a hysteresis strategy to manage structural changes and guarantee finite-time switching.
Main Results:
- Successful automated selection of model structures.
- Accurate identification of model parameters despite uncertainties.
- Effective handling of structural changes with guaranteed performance.
- Demonstration of a novel and integrated approach to system identification.
Conclusions:
- The proposed integrated framework offers an effective solution for automated structure selection and parameter identification.
- Fuzzy neural networks, combined with kernel smoothing and hysteresis strategies, provide a robust approach to system identification under uncertainty.
- This method enhances the efficiency and reliability of identifying dynamic systems.
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