This paper introduces the Adaptive Rational Filter Selection (ARFS) framework to address challenges in processing non-stationary signals. ARFS dynamically selects optimal rational filter configurations based on real-time spectral distribution estimation, combining multi-resolution spectral analysis, a configurable rational filter bank, and a reinforcement learning-based selection mechanism.
Key findings
ARFS dynamically selects optimal rational filter configurations for varying spectral distributions.
The framework includes a multi-resolution spectral analyzer, a configurable rational filter bank, and a reinforcement learning-based selection mechanism.
Theoretical analysis establishes convergence guarantees and computational complexity bounds.
ARFS outperforms state-of-the-art adaptive filtering methods in applications like image denoising, audio enhancement, and biomedical signal processing.
Limitations & open questions
The paper does not discuss the scalability of the ARFS framework for very high-dimensional spectral data.
Further research is needed to enhance the framework's robustness in highly variable signal environments.