Analysis of Identification Using Orthogonal Basis Functions: Performance Evaluation
Published:Dec 24, 2025 10:35
•1 min read
•ArXiv
Analysis
This research paper explores the convergence speed, asymptotic bias, and optimal pole selection within the context of identification using orthogonal basis functions, a crucial aspect of signal processing and machine learning. Its contribution lies in providing a rigorous mathematical analysis for selecting poles in basis functions, which will help achieve the optimal performance in such identification tasks.
Key Takeaways
- •Focuses on a theoretical analysis of identification using orthogonal basis functions.
- •Investigates the impact of pole selection on performance metrics such as convergence and bias.
- •Aims to provide theoretical foundation for designing and optimizing identification systems.
Reference
“The research focuses on convergence speed, asymptotic bias, and rate-optimal pole selection.”