Results 241 to 250 of about 1,005,453 (288)

Frequency domain adaptive learning feedforward control

Proceedings 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation (Cat. No.01EX515), 2002
A learning feedforward control (LFFC) acts as an "add-on" element to an existing feedback controller (FBC). The LFFC signal is updated iteratively based on the output of a B-splines network (BSN), with the FBC signal of previous iteration as its input. There are only two parameters to tune: the B-spline support width d and the learning gain /spl gamma/.
YangQuan Chen, Kevin L. Moore 0001
openaire   +1 more source

Parametric Iterative Learning Control in the time and frequency domain

International Journal of Intelligent Systems Technologies and Applications, 2007
For the control of cyclic processes Iterative Learning Control (ILC) has been proven to be an efficient concept. In this contribution a parametric framework is proposed that uses an analogy to a discrete time control loop and intentionally avoids highly theoretical concepts such as two dimensional systems theory.
Hellmar Rockel, Ulrich Konigorski
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Frequency domain analysis of learning systems

Proceedings of the 27th IEEE Conference on Decision and Control, 2003
Frequency domain arguments on a general learning law are presented to verify claims of convergence which relax earlier requirements on the learning law transfer function. Known convergence properties are used to develop a pole-placement interpretation for an example for which simulations using a proportional-derivative learning law form are carried out.
L.M. Hideg, R.P. Judd
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A Frequency Domain Analysis of Learning Control

Journal of Dynamic Systems, Measurement, and Control, 1994
The convergence of learning control is traditionally analyzed in the time domain. This is because a finite planning horizon is often assumed and the analysis in time domain can be extended to time-varying and nonlinear systems. For linear time-invariant (LTI) systems with infinite planning horizon, however, we show that simple frequency domain ...
openaire   +2 more sources

Time–Frequency-Domain Deep Learning Framework for the Automated Detection of Heart Valve Disorders Using PCG Signals

IEEE Transactions on Instrumentation and Measurement, 2022
The damage to the heart valves causes heart valve disorders (HVDs). The detection of HVDs is crucial in a clinical study as these diseases may cause congestive heart failure, hypertrophy, and stroke.
Jay Karhade, Shaswati Dash, S. Ghosh, Dinesh Kumar Dash, R. Tripathy   +4 more
semanticscholar   +1 more source

A FREQUENCY-DOMAIN MODEL FOR E-LEARNING

eLearning and Software for Education, 2020
The characteristics of e-learning make it suitable to the Systems Theory approach. A system is an ensemble of entities which interact among themselves and with the exterior, in order to reach an objective. Clearly, e-learning integrates with this definition.
openaire   +1 more source

Decorrelating the Future: Joint Frequency Domain Learning for Spatio-temporal Forecasting

arXiv.org
Standard direct forecasting models typically rely on point-wise objectives such as Mean Squared Error, which fail to capture the complex spatio-temporal dependencies inherent in graph-structured signals.
Zepu Wang, Bowen Liao, J. Ban
semanticscholar   +1 more source

Frequency-Domain Decomposition and Deep Learning Based Solar PV Power Ultra-Short-Term Forecasting Model

IEEE transactions on industry applications, 2021
Ultra-short-term photovoltaic (PV) power forecasting can support the real-time dispatching of the power grid. However, PV power has great fluctuations due to various meteorological factors, which increase energy prices and cause difficulties in managing ...
Jichuan Yan, Lin Hu, Z. Zhen, Fei Wang, G. Qiu, Yu Li, L. Yao, M. Shafie‐khah, J. Catalão   +8 more
semanticscholar   +1 more source

Filter Pruning via Learned Representation Median in the Frequency Domain

IEEE Transactions on Cybernetics, 2023
In this article, we propose a novel filter pruning method for deep learning networks by calculating the learned representation median (RM) in frequency domain (LRMF). In contrast to the existing filter pruning methods that remove relatively unimportant filters in the spatial domain, our newly proposed approach emphasizes the removal of absolutely ...
Xin Zhang 0092, Weiying Xie, Yunsong Li 0001, Jie Lei 0001, Qian Du 0001   +4 more
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Learning Orientation Information From Frequency-Domain for Oriented Object Detection in Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing, 2022
Object detection in remote sensing images (RSIs) poses great difficulties due to arbitrary orientations, various scales, and dense location of the targets over the ground. Recent evidence suggests that encoding the orientation information is of great use
Shangdong Zheng, Zebin Wu, Yang Xu, Zhihui Wei, A. Plaza   +4 more
semanticscholar   +1 more source