《Data-Driven Science and Engineering:Machine Learning, Dynamical Systems, and Control》,作者是华盛顿大学的Steven L. Brunton和J. Nathan Kutz, 全书共分为4个Part:降维与变换、机器学习和数据分析、动力学和控制、降阶模型,如果有需要pdf版本的同学可以私信我 最常见的优化策略 Least-Squares 最小二乘使给定...
今天推荐 "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" 一书的第二版,新增了不少内容。 官网链接: Data-Driven Science and Engineering | Higher Educat…
内容提示: Data-Driven Science and EngineeringData-driven discovery is revolutionizing the modeling, prediction, and control of complexsystems. This textbook brings together machine learning, engineering mathematics, andmathematical physics to integrate modeling and control of dynamical systems with modern...
Reservoir computing (RC) is a machine learning technique that builds on dynamical systems theory and provides the basis of the team's approach. RC is used to control a type of neural network called a recurrent neural network (RNN). Unlike other machine learning approaches that tune all neural ...
S. L. Brunton and J. N. Kutz. Data-Driven Science and Engineering: Machine Learning, Dynamical Sys- tems, and Control. Cambridge University Press, 2018.Brunton, S. L. & Kutz, J. N.: Data-driven science and engineering: Machine learning, dynamical systems, and control, Cambridge Univ. ...
【预订】Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control 预订商品,需要1-3个月发货,非质量问题不接受退换货。¥868.68 降价通知 暂无评分 1人评分精彩评分送积分 送至:北京>北京市>东城区进口书预订,需要1-3个月发货...
19-数据驱动科学与工程机器学习、动力系统和控制-Data-Driven Science and Engineering Machine Learning, Dynamical Systems, and Control,机器学习和数据科学,Science,engine,人人文库,
Data-driven discovery is revolutionizing the modeling,prediction,and control of complex systems. This textbook brings together machine learning,engineering mathematics,and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of th...
machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, ...
Nonlinear dynamical systems, which include models of the Earth's climate, financial markets and complex ecosystems, often undergo abrupt transitions that lead to radically different behavior. The ability to predict such qualitative and potentially disruptive changes is an important problem with far-reachin...