A Dual Averaging Algorithm for Online Modeling of Infinite Memory Nonlinear Systems

• 成果类型: Article
• 署名作者: Lagosz, Szymon; Wachel, Pawel; Sliwinski, Przemyslaw
• 署名单位: Wroclaw University of Science & Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3225506
• 发表日期: 2023
• 页码: 5677-5684
• 关键词: Nonlinear systems; optimization algorithms; Stochastic systems; volterra series

摘要

An online modeling algorithm is derived from a generic stochastic dual averaging (DA) method. It employs a negative entropy as a distance-generating function and the Volterra series expansion as a dictionary. Assuming that the measurement data are not i.i.d. but generated by a nonlinear dynamical system with an infinite, exponentially fading memory, the error bounds are established for both the generic DA method and for the proposed modeling algorithm. The experiments performed on a set of benchmark systems confirm the applicability of the algorithm in real-world scenarios and demonstrate its low computational complexity.