《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...
We achieve this for autonomous and for periodically forced systems of finite or infinite dimensions by constructing linearizing transformations for their dominant dynamics within attracting slow spectral submanifolds (SSMs). Our arguments also lead to a new algorithm, data-driven linearization (DDL), ...
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…
We present a data-driven realization for systems with delay, which generalizes the Loewner framework. The realization is obtained with low computational cost directly from measured data of the transfer function. The internal delay is estimated by solving a least-square optimization over some sample da...
State-of-the-art methods for data-driven modelling of non-linear dynamical systems typically involve interactions with an expert user. In order to partially automate the process of modelling physical systems from data, many EA-based approaches have been proposed for model-structure selection, with ...
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 how we model, predict, and control complex systems. Now with MATLAB, Data-Driven Science and Engineering trains mathematical scientists and engineers for the next generation of scientific discovery by offering a b
N. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (Cambridge University Press, 2019). Lewis, F. L., Vrabie, D. & Vamvoudakis, K. G. Reinforcement learning and feedback control: using natural decision methods to design optimal adaptive controllers. IEEE ...
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 the recent advances in scientific computing that enable data-driven m.....
There are various methods for identifying non-hybrid dynamical systems. Schmidt and Lipson7 propose a data-driven approach to determine the underlying structure and parameters of time-invariant nonlinear dynamical systems. Schmidt and Lipson’s method uses symbolic regression to identify the system, bala...