Results 61 to 70 of about 1,153,224 (274)

Polynomial Iterative Learning Control (ILC) Tracking Control Design for Uncertain Repetitive Continuous-Time Linear Systems Applied to an Active Suspension of a Car Seat

open access: yesMathematics
This paper addresses the issue of polynomial iterative learning tracking control (Poly-ILC) for continuous-time linear systems (LTI) operating repetitively.
Selma Ben Attia   +4 more
doaj   +1 more source

Learning a DFT-based sequence with reinforcement learning: a NAO implementation

open access: yesPaladyn, 2012
The implementation of sequence learning in robotic platforms offers several challenges. Deciding when to stop one action and continue to the next requires a balance between stability of sensory information and, of course, the knowledge about what action ...
Durán Boris, Lee Gauss, Lowe Robert
doaj   +1 more source

Stability-Guaranteed Reinforcement Learning for Contact-rich Manipulation [PDF]

open access: yesarXiv, 2020
Reinforcement learning (RL) has had its fair share of success in contact-rich manipulation tasks but it still lags behind in benefiting from advances in robot control theory such as impedance control and stability guarantees. Recently, the concept of variable impedance control (VIC) was adopted into RL with encouraging results.
arxiv  

Sample-Conditioned Hypothesis Stability Sharpens Information-Theoretic Generalization Bounds [PDF]

open access: yesarXiv, 2023
We present new information-theoretic generalization guarantees through the a novel construction of the "neighboring-hypothesis" matrix and a new family of stability notions termed sample-conditioned hypothesis (SCH) stability. Our approach yields sharper bounds that improve upon previous information-theoretic bounds in various learning scenarios ...
arxiv  

Data‐driven forecasting of ship motions in waves using machine learning and dynamic mode decomposition

open access: yesInternational Journal of Adaptive Control and Signal Processing, EarlyView.
Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez   +2 more
wiley   +1 more source

Exploration and Exploitation of New Knowledge Emergence to Improve the Collective Intelligent Decision-Making Level of Web-of-Cells With Cyber-Physical-Social Systems Based on Complex Network Modeling

open access: yesIEEE Access, 2018
Through exploration and exploitation of new knowledge emergence, the collective intelligent decision-making (CID) level of Web-of-Cells (WoC) proposed by ELECTRA will be dramatically improved.
Lefeng Cheng, Tao Yu
doaj   +1 more source

Advances in Hybrid Icing and Frosting Protection Strategies for Optics, Lens, and Photonics in Cold Environments Using Thin‐Film Acoustic Waves

open access: yesAdvanced Engineering Materials, EarlyView.
This article provides a comprehensive overview of fundamentals and recent advances of transparent thin‐film surface acoustic wave technologies on glass substrates for monitoring and prevention/elimination of fog, ice, and frost. Fogging, icing, or frosting on optical lenses, optics/photonics, windshields, vehicle/airplane windows, and solar panel ...
Hui Ling Ong   +11 more
wiley   +1 more source

Design performance of lead and lag compensator using OPAMP and root locus approach through simulation tool

open access: yesITEGAM-JETIA, 2020
The design objective behind lead and lag compensator is to meet the relative stability as well as to meet desired performance. Both in time domain or frequency domain, the compensator design can be carried out.
Badri Narayan Mohapatra, Jijnyasa Joshi
doaj   +1 more source

Consolidate Overview of Ribonucleic Acid Molecular Dynamics: From Molecular Movements to Material Innovations

open access: yesAdvanced Engineering Materials, EarlyView.
Molecular dynamics simulations are advancing the study of ribonucleic acid (RNA) and RNA‐conjugated molecules. These developments include improvements in force fields, long‐timescale dynamics, and coarse‐grained models, addressing limitations and refining methods.
Kanchan Yadav, Iksoo Jang, Jong Bum Lee
wiley   +1 more source

Black-box tests for algorithmic stability [PDF]

open access: yesarXiv, 2021
Algorithmic stability is a concept from learning theory that expresses the degree to which changes to the input data (e.g., removal of a single data point) may affect the outputs of a regression algorithm. Knowing an algorithm's stability properties is often useful for many downstream applications -- for example, stability is known to lead to desirable
arxiv  

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