Improving nonlinear modeling capabilities of functional link adaptive filters.
Danilo Comminiello1, Michele Scarpiniti1, Simone Scardapane1
1Department of Information Engineering, Electronics and Telecommunications (DIET), "Sapienza" University of Rome, Via Eudossiana 18, 00184 Rome, Italy.
Summary
This study introduces an improved functional link adaptive filter (FLAF) architecture for nonlinear modeling. The enhanced nonlinear branch improves cooperative behavior and performance, especially with strong nonlinearities.
Area of Science:
- Signal Processing
- Machine Learning
- Adaptive Systems
Background:
- Online nonlinear modeling is crucial for many dynamic systems.
- Existing functional link adaptive filters (FLAFs) offer a robust framework.
- Performance limitations arise in handling strong nonlinearities within FLAFs.
Purpose of the Study:
- To enhance the nonlinear modeling performance of FLAF architectures.
- To introduce a novel FLAF model with an adaptive filter combination in the nonlinear branch.
- To develop an advanced architecture utilizing multiple adaptive filters for improved cooperative behavior.
Main Methods:
- Developing a FLAF architecture with separated linear and nonlinear adaptation.
- Proposing a new nonlinear branch incorporating an adaptive combination of downstream filters.
- Implementing and evaluating an advanced architecture with multiple adaptive filters.
- Testing the models on diverse nonlinear modeling problems.
Main Results:
- The proposed adaptive filter combination in the nonlinear branch significantly improves modeling performance.
- The cooperative behavior of the enhanced architecture effectively handles strong nonlinearities.
- The advanced architecture demonstrates superior capabilities in complex nonlinear scenarios.
- Experimental results validate the effectiveness of the proposed FLAF models.
Conclusions:
- The enhanced FLAF architecture with adaptive filter combinations offers superior nonlinear modeling capabilities.
- The proposed approach provides a significant performance improvement, particularly for systems with strong nonlinearities.
- This work contributes a more effective and robust solution for online nonlinear modeling challenges.
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