Identification with Orthogonal Basis Functions: Convergence Speed, Asymptotic Bias, and Rate-Optimal Pole Selection

TL;DR

This paper analyzes the convergence, bias, and pole selection in orthogonal basis function-based system identification, proposing an asymptotically optimal pole selection algorithm with proven bounds and exponential bias decay.

Contribution

It introduces a pole selection algorithm that minimizes identification bias and proves its asymptotic optimality with explicit bounds and convergence analysis.

Findings

Bias decreases exponentially with basis functions

Proposed algorithm achieves the fundamental bias lower bound

Numerical results validate theoretical bounds and effectiveness

Abstract

This paper is concerned with performance analysis and pole selection problem in identifying linear time-invariant (LTI) systems using orthogonal basis functions (OBFs), a system identification approach that consists of solving least-squares problems and selecting poles within the OBFs. Specifically, we analyze the convergence properties and asymptotic bias of the OBF algorithm, and propose a pole selection algorithm that robustly minimizes the worst-case identification bias, with the bias measured under the $\mathcal{H}_2$ error criterion. Our results include an analytical expression for the convergence rate and an explicit bound on the asymptotic identification bias, which depends on both the true system poles and the preselected model poles. Furthermore, we demonstrate that the pole selection algorithm is asymptotically optimal, achieving the fundamental lower bound on the identification bias. The algorithm explicitly determines the model poles as the so-called Tsuji points, and the asymptotic identification bias decreases exponentially with the number of basis functions, with the rate of decrease governed by the hyperbolic Chebyshev constant. Numerical experiments validate the derived bounds and demonstrate the effectiveness of the proposed pole selection algorithm.