Fast Iterative Sample Transfer Identification Method for Dynamic Systems Under Non-identical Distribution

This paper proposes a method to improve the identification performance of linear dynamic systems by utilizing knowledge from samples of non-identical distribution systems. Traditional identification methods heavily rely on the quality of the dataset, such as sample length and noise level, which constrains their performance due to the assumption of identical distribution. Motivated by the concept of sample-based transfer learning, we propose a sample transfer identification method and derive the condition to avoid negative transfer. We develop a fast iterative transfer identification method for low storage costs, considering the computational burden imposed by the sample size from the source system. Additionally, based on the fast iterative transfer identification method, considering the need to update the current measurement data model in real time, a fast iterative online sample transfer identification method is explored. Through simulations, we validate the effectiveness and superiority of the proposed methods. The results show that sample transfer identification is superior to non-transfer identification and fast iterative sample transfer identification effectively reduces the calculation amount when dealing with low quality measurement data.