首页|Reports from Xiangtan University Describe Recent Advances in Machine Learning (B ackward Differentiation Formula Method and Random Forest Method To Solve Continu ous-time Differential Riccati Equations)
Reports from Xiangtan University Describe Recent Advances in Machine Learning (B ackward Differentiation Formula Method and Random Forest Method To Solve Continu ous-time Differential Riccati Equations)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news originating from Xiangtan, People’s Republic o f China, by NewsRx correspondents, research stated, “In this paper, we explore t he utilization of machine learning techniques for solving the numerical solution s of continuoustime differential Riccati equations.”Financial support for this research came from National Key Research & Development Program of China. Our news journalists obtained a quote from the research from Xiangtan University , “Specifically, we focus on generating a reduction matrix capable of transformi ng a high-order matrix into a low-order matrix. Additionally, we address the iss ue of differential terms in the continuous-time differential Riccati equation an d incorporate the backward differentiation formula of the matrix to improve stab ility and accuracy.”
XiangtanPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningXiangtan University
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