首页|Research Study Findings from Harbin Institute of Technology Up- date Understanding of Machine Learning (Review of the application of modeling and estimation method in system identification for non- linear state-space models)
Research Study Findings from Harbin Institute of Technology Up- date Understanding of Machine Learning (Review of the application of modeling and estimation method in system identification for non- linear state-space models)
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NETL
NSTL
World Scientific
2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intelligence have been published. According to news originating from Harbin, People’s Republic of China, by NewsRx editors, the research stated, “Nonlinear state-space models (SSMs) are widely used to model actual industrial processes. System identification is an important method to reduce the uncertainty of the simulation model.” Financial supporters for this research include National Natural Science Foundation of China. The news reporters obtained a quote from the research from Harbin Institute of Technology: “In recent years, system identification has been greatly improved with the rise of machine learning. However, there are a few reviews on the latest identification methods based on machine learning. Therefore, this paper focuses on the latest development of identification methods for nonlinear SSM in recent years. In particular, this paper comprehensively compares the identification methods based on traditional methods and machine learning. In addition, according to the type of uncertainty, we divided the paper into the parameter’s identification and the identification of unknown parts of the model. Compared with the classification of other reviews, our classification method is clearer. Briefly, this paper organizes the review according to the classification of uncertainty. Each type is extended from offline identification to online identification. Specifically, interval identification and point estimation methods are reviewed for offline parameter identification. For online parameter identification, point estimation methods are reviewed.”
Harbin Institute of TechnologyHarbinPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
World Scientific
