Results 41 to 50 of about 8,464,096 (315)

Self-tuning method for a linear active disturbance rejection controller

工程科学学报, 2015
This paper introduces a parameter self-tuning algorithm based on dynamic response time series data mining to solve the parameter self-tuning difficulty of a linear active disturbance rejection controller(LADRC).
LI Yang, WANG Jing, ZHANG Yong-jun
doaj   +1 more source

Effectiveness and limitations of parameter tuning in reducing biases of top-of-atmosphere radiation and clouds in MIROC version 5 [PDF]

Geoscientific Model Development, 2017
This study discusses how much of the biases in top-of-atmosphere (TOA) radiation and clouds can be removed by parameter tuning in the present-day simulation of a climate model in the Coupled Model Inter-comparison Project phase 5 (CMIP5) generation ...
T. Ogura, H. Shiogama, M. Watanabe, M. Yoshimori, T. Yokohata, J. D. Annan, J. C. Hargreaves, N. Ushigami, K. Hirota, Y. Someya, Y. Kamae, H. Tatebe, M. Kimoto   +12 more
doaj   +1 more source

Parameter Tuning for Local-Search-Based Matheuristic Methods

Complexity, 2017
Algorithms that aim to solve optimisation problems by combining heuristics and mathematical programming have attracted researchers’ attention. These methods, also known as matheuristics, have been shown to perform especially well for large, complex ...
Guillermo Cabrera-Guerrero, Carolina Lagos, Carolina Castañeda, Franklin Johnson, Fernando Paredes, Enrique Cabrera   +5 more
doaj   +1 more source

Quadrature Signal Generator with Improved Dc Offset Compensation

Advances in Electrical and Computer Engineering, 2021
In this paper a second order generalized integrator (SOGI) based quadrature signal generator (QSG) is proposed with the improved DC offset compensation parameter tuning procedure.
STOJIC, D.
doaj   +1 more source

Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity [PDF]

International Conference on Medical Imaging with Deep Learning, 2023
Foundation models have significantly advanced medical image analysis through the pre-train fine-tune paradigm. Among various fine-tuning algorithms, Parameter-Efficient Fine-Tuning (PEFT) is increasingly utilized for knowledge transfer across diverse ...
Raman Dutt, Linus Ericsson, Pedro Sanchez, S. Tsaftaris, Timothy M. Hospedales   +4 more
semanticscholar   +1 more source

A precision study of the fine tuning in the DiracNMSSM [PDF]

, 2013
Recently the DiracNMSSM has been proposed as a possible solution to reduce the fine tuning in supersymmetry. We determine the degree of fine tuning needed in the DiracNMSSM with and without non-universal gaugino masses and compare it with the fine tuning
A Arvanitaki, A Belyaev, A Brignole, A Brignole, A Dedes, A Dedes, A Delgado, A Delgado, A Kaminska, A Semenov, Anna Kaminska, ATLAS collaboration, CMS collaboration, D Chowdhury, D Horton, DM Ghilencea, DM Pierce, DS Akerib, E Hardy, E Hardy, E Ma, F Franke, F Lyonnet, F Staub, F Staub, F Staub, F Staub, F Staub, F Staub, Florian Staub, G Bélanger, G Bélanger, G Bélanger, G Bélanger, G Degrassi, G Degrassi, GG Ross, GG Ross, Graham G. Ross, H Abe, H Abe, H Dreiner, HM Lee, HM Lee, I Gogoladze, I Jack, JE Camargo-Molina, JR Ellis, K Benakli, K Choi, K Choi, K Ender, K Schmidt-Hoberg, K Schmidt-Hoberg, Kai Schmidt-Hoberg, LJ Hall, M Asano, M Badziak, M Bastero-Gil, M Dine, M Hirsch, M Sperling, M Sperling, MD Goodsell, MS Carena, N Blinov, ND Christensen, R Barbieri, R Dermisek, R Dermisek, R Dermisek, RM Fonseca, RM Fonseca, S Antusch, S Cassel, S Krippendorf, SA Abel, SF King, SP Martin, T Elliott, T Elliott, T Graf, TT Yanagida, U Ellwanger, U Ellwanger, U Ellwanger, U Ellwanger, W Porod, W Porod, X Lu, Y Yamada, Y Zhang   +91 more
core   +2 more sources

Parameter-Efficient Fine-Tuning without Introducing New Latency [PDF]

Annual Meeting of the Association for Computational Linguistics, 2023
Parameter-efficient fine-tuning (PEFT) of pre-trained language models has recently demonstrated remarkable achievements, effectively matching the performance of full fine-tuning while utilizing significantly fewer trainable parameters, and consequently ...
Baohao Liao, Yan Meng, C. Monz
semanticscholar   +1 more source

Optimization of Neural Network-Based Self-Tuning PID Controllers for Second Order Mechanical Systems

Applied Sciences, 2021
The feasibility of a neural network method was discussed in terms of a self-tuning proportional–integral–derivative (PID) controller. The proposed method was configured with two neural networks to dramatically reduce the number of tuning attempts with a ...
Yong-Seok Lee, Dong-Won Jang
doaj   +1 more source

Calibrating the GAMIL3-1° climate model using a derivative-free optimization method [PDF]

Geoscientific Model Development
Parameterization in climate models often involves parameters that are poorly constrained by observations or theoretical understanding alone. Manual tuning by experts can be time-consuming, subjective, and prone to underestimating uncertainties. Automated
W. Liang, W. Liang, W. Liang, S. F. B. Tett, L. Li, C. Cartis, D. Xu, W. Dong, W. Dong, J. Huang, J. Huang   +10 more
doaj   +1 more source

High dimensional parameter tuning for event generators

European Physical Journal C: Particles and Fields, 2020
Monte Carlo Event Generators are important tools for the understanding of physics at particle colliders like the LHC. In order to best predict a wide variety of observables, the optimization of parameters in the Event Generators based on precision data ...
Johannes Bellm, Leif Gellersen
doaj   +1 more source