On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection

2008 ◽  
pp. 91-107 ◽  
Author(s):  
R. Alcalá ◽  
J. Alcalá-Fdez ◽  
M. J. Gacto ◽  
F. Herrera
Keyword(s):  
Parameter Tuning ◽  
Rule Selection

Development of Automatic Parameter Tuning for Train Automatic Stop Control Device

2016 ◽  
Vol 136 (9) ◽  
pp. 629-634
Author(s):  
Atsushi Oda ◽  
Ikuo Niimi ◽  
Kentaro Maki
Keyword(s):  
Parameter Tuning ◽  
Control Device ◽  
Automatic Parameter Tuning

Application of improved PSO for parameter tuning of PID controller

2010 ◽  
Vol 24 (2) ◽  
pp. 141-146 ◽  
Author(s):  
Cuiyun Jin ◽  
Jianlin Wang ◽  
Jiangning Ma ◽  
Ying Wang
Keyword(s):  
Pid Controller ◽  
Parameter Tuning ◽  
Improved Pso

Theoretical Analysis of Image Processing Using Parameter-Tuning Stochastic Resonance Technique

2006 ◽  
Author(s):  
Bohou Xu ◽  
Xingxing Wu ◽  
Zhong-Ping Jiang ◽  
Daniel W. Repperger
Keyword(s):  
Image Processing ◽  
Theoretical Analysis ◽  
Stochastic Resonance ◽  
Parameter Tuning ◽  
Resonance Technique

scholarly journals Variable Selection and Parameter Tuning for BART Modeling in the Fragile Families Challenge

2019 ◽  
Vol 5 ◽  
pp. 237802311982588 ◽  
Author(s):  
Nicole Bohme Carnegie ◽  
James Wu
Keyword(s):  
Variable Selection ◽  
Missing Values ◽  
Linear Models ◽  
Parameter Tuning ◽  
Black Box ◽  
Machine Learning Algorithms ◽  
Outcome Variable ◽  
Fragile Families ◽  
Great Flexibility ◽  
Additive Regression

Our goal for the Fragile Families Challenge was to develop a hands-off approach that could be applied in many settings to identify relationships that theory-based models might miss. Data processing was our first and most time-consuming task, particularly handling missing values. Our second task was to reduce the number of variables for modeling, and we compared several techniques for variable selection: least absolute selection and shrinkage operator, regression with a horseshoe prior, Bayesian generalized linear models, and Bayesian additive regression trees (BART). We found minimal differences in final performance based on the choice of variable selection method. We proceeded with BART for modeling because it requires minimal assumptions and permits great flexibility in fitting surfaces and based on previous success using BART in black-box modeling competitions. In addition, BART allows for probabilistic statements about the predictions and other inferences, which is an advantage over most machine learning algorithms. A drawback to BART, however, is that it is often difficult to identify or characterize individual predictors that have strong influences on the outcome variable.

scholarly journals Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 955
Author(s):  
Zhiyuan Li ◽  
Ershuai Peng
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Dynamic Network ◽  
Parameter Tuning ◽  
Global Optimum ◽  
Research Field ◽  
Edge Computing ◽  
Transmission Delay ◽  
Smart Vehicles ◽  
Network Bandwidth

With the development of smart vehicles and various vehicular applications, Vehicular Edge Computing (VEC) paradigm has attracted from academic and industry. Compared with the cloud computing platform, VEC has several new features, such as the higher network bandwidth and the lower transmission delay. Recently, vehicular computation-intensive task offloading has become a new research field for the vehicular edge computing networks. However, dynamic network topology and the bursty computation tasks offloading, which causes to the computation load unbalancing for the VEC networking. To solve this issue, this paper proposed an optimal control-based computing task scheduling algorithm. Then, we introduce software defined networking/OpenFlow framework to build a software-defined vehicular edge networking structure. The proposed algorithm can obtain global optimum results and achieve the load-balancing by the virtue of the global load status information. Besides, the proposed algorithm has strong adaptiveness in dynamic network environments by automatic parameter tuning. Experimental results show that the proposed algorithm can effectively improve the utilization of computation resources and meet the requirements of computation and transmission delay for various vehicular tasks.

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