The mathematics and control in filtering problem
演讲者
时间
2025年02月28日 15:00 至 16:00
地点
A3-2a-302
线上
Zoom 637 734 0280
(BIMSA)
摘要
In practical estimation applications, observational data from physical models are invariably affected by various forms of noise. In the current data-driven era, extracting and recovering meaningful information has emerged as a critical challenge. Filtering theory, which combines time-series observational data with prior knowledge of physical models, provides a framework for addressing these estimation problems. This theory is widely applicable to sequential tasks in fields such as communications, finance, navigation, image processing, and geophysics. Traditional approaches to solving filtering problems generally rely on two methodological frameworks. The first involves formulating stochastic differential equations through the theory of stochastic partial differential equations, followed by applying analytical theories and numerical algorithms to derive closed-form solutions for specific cases or approximate solutions for broader scenarios. The second employs particle-based techniques, such as Monte Carlo methods, to reconstruct statistical properties of system states or approximate posterior density functions using particle ensembles. Recent advancements in artificial intelligence have enabled data-driven and AI-powered strategies to design more efficient filtering algorithms, opening new avenues for addressing complex industrial and financial challenges in real-world settings.