Results 271 to 280 of about 1,466,211 (327)

A NOTE ON STABILITY OF ERROR BOUNDS IN STATISTICAL LEARNING THEORY [PDF]

Analysis and Applications, 2011
We consider a wide class of error bounds developed in the context of statistical learning theory which are expressed in terms of functionals of the regression function, for instance, its norm in a reproducing kernel Hilbert space or other functional space.
Ming Li, Andrea Caponnetto
openalex   +2 more sources

Stability theory of universal learning network

1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929), 2002
Higher order derivatives of the universal learning network (ULN) has been previously derived by forward and backward propagation computing methods, which can model and control the large scale complicated systems such as industrial plants, economic, social and life phenomena. In this paper, a new concept of nth order asymptotic orbital stability for the
Kotaro Hirasawa, M. Ohbayashi, Masashi Koga, Naohiro Kusumi   +3 more
openalex   +3 more sources

Analysis of robust control using stability theory of universal learning networks

IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028), 2003
Nth order asymptotic orbital stability analysis method has been proposed to determine whether a nonlinear system is stable or not with large fluctuations of the system states. In this paper, we discuss the stability of robust control of a nonlinear crane system using this method.
Yunqing Yu, Kotaro Hirasawa, Jinglu Hu, Junichi Murata   +3 more
openalex   +3 more sources

Some of the next articles are maybe not open access.

Related searches:

Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult

arXiv.org, 2023
Large learning rates, when applied to gradient descent for nonconvex optimization, yield various implicit biases including the edge of stability (Cohen et al., 2021), balancing (Wang et al., 2022), and catapult (Lewkowycz et al., 2020).
Yuqing Wang, Zhenghao Xu, Tuo Zhao, Molei Tao   +3 more
semanticscholar   +1 more source

Safe Reinforcement Learning With Stability Guarantee for Motion Planning of Autonomous Vehicles

IEEE Transactions on Neural Networks and Learning Systems, 2021
Reinforcement learning with safety constraints is promising for autonomous vehicles, of which various failures may result in disastrous losses. In general, a safe policy is trained by constrained optimization algorithms, in which the average constraint ...
Lixian Zhang, Ruixian Zhang, Tong Wu, Rui Weng, Minghao Han, Ye Zhao   +5 more
semanticscholar   +1 more source

Exploring the Effects of Ionic Defects on the Stability of CsPbI3 with a Deep Learning Potential.

ChemPhysChem, 2022
Inorganic metal halide perovskites, such as CsPbI3 , have recently drawn extensive attention due to their excellent optical properties and high photoelectric efficiencies.
Weijie Yang, Jiajia Li, Xuelu Chen, Yajun Feng, Chongchong Wu, I. Gates, Zhengyang Gao, Xunlei Ding, Jianxi Yao, Hao Li   +9 more
semanticscholar   +1 more source

Iterative learning control for discrete linear systems with Zero Markov parameters using repetitive process stability theory

2011 IEEE International Symposium on Intelligent Control, 2011
This paper considers iterative learning control for the practically relevant case of deterministic discrete linear plants where the first Markov parameter is zero. A 2D systems approach that uses a strong form of stability for linear repetitive processes is used to develop a one step control law design for both trial-to-trial error convergence and ...
Łukasz Hładowski, Krzysztof Gałkowski, Eric Rogers, Zhonglun Cai, Christopher Freeman, P. L. Lewin   +5 more
openalex   +3 more sources

Experimentally verified Iterative Learning Control based on repetitive process stability theory

2012 American Control Conference (ACC), 2012
This paper gives new results on the design and experimental evaluation of an Iterative Learning Control (ILC) law in a repetitive process setting. The experimental results given are from a gantry robot facility that has been extensively used in the benchmarking of linear model based ILC designs.
Dabkowski, Galkowski, C. Paul Rogers, Cai, Freeman, Lewin, Hurak, Kummert   +7 more
  +5 more sources

Thermodynamic stability of Pd–Ru alloy nanoparticles: combination of density functional theory calculations, supervised learning, and Wang–Landau sampling

Physical Chemistry Chemical Physics, 2022
Composition, size, and structure dependences of stable configuration of Pd–Ru alloy nanoparticles under finite temperature were theoretically investigated by using density functional theory calculation, multiple regression, and Wang–Landau sampling.
Yūsuke Nanba, Michihisa Koyama
openalex   +3 more sources

Data-Driven Insight into the Reductive Stability of Ion-Solvent Complexes in Lithium Battery Electrolytes.

Journal of the American Chemical Society, 2023
Lithium (Li) metal batteries (LMBs) are regarded as one of the most promising energy storage systems due to their ultrahigh theoretical energy density.
Yu‐Chen Gao, Nana Yao, Xiang Chen, Legeng Yu, Rui Zhang, Qian Zhang   +5 more
semanticscholar   +1 more source