Introducing adaptive neurofuzzy modeling with online learning method for prediction of time-varying solar and geomagnetic activity indices

2010 ◽  
Vol 37 (12) ◽  
pp. 8267-8277 ◽  
Author(s):  
Masoud Mirmomeni ◽  
Caro Lucas ◽  
Behzad Moshiri ◽  
Babak Nadjar Araabi
Solar Physics ◽  
2011 ◽  
Vol 272 (1) ◽  
pp. 189-213 ◽  
Author(s):  
Masoud Mirmomeni ◽  
Caro Lucas ◽  
Babak Nadjar Araabi ◽  
Behzad Moshiri ◽  
Mohammad Reza Bidar

Author(s):  
Yunni Susanty

The COVID 19 pandemic also has an impact on the education and training aspects of the State Civil Apparatus. MOT training in Puslatbang PKASN LAN, which was originally carried out by blended learning in 2019, has been changed to fully online learning in 2020, as an effort to reduce the spread of the COVID 19 virus. The purpose of this study is to find out whether there are differences on the learning outcomes between MOT participants in 2019, which attended by 30 people, and MOT participants in 2020, which attended by 25 people. Data processing and analysis techniques in this study using quantitative methods. The statistical test used is the non-parametric statistical test using the Mann Whitney U test. The sampling technique used was total sampling, where all members of the population were used as samples. The results revealed that there was no difference in the learning outcomes of MOT participants between those using the blended learning method and those using the fully online learning method. Based on this information, fully online learning is very possible to be applied. Nevertheless, the Training Institution must pay attention to the availability of facilities and infrastructure that support the learning process electronically. Also, the limited interaction between lecturers and participants when doing online learning should be balanced with the ability of lecturers to convey material with technology-based learning techniques. In this case, the roles of all parties will determine the optimal achievement of the fully online learning process.


2015 ◽  
Vol 55 (4) ◽  
pp. 493-498 ◽  
Author(s):  
A. A. Nusinov ◽  
N. M. Rudneva ◽  
E. A. Ginzburg ◽  
L. A. Dremukhina

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Dongdong Mu ◽  
Guofeng Wang ◽  
Yunsheng Fan ◽  
Yiming Bai ◽  
Yongsheng Zhao

This paper investigates the path following control problem for an underactuated unmanned surface vehicle (USV) in the presence of dynamical uncertainties and time-varying external disturbances. Based on fuzzy optimization algorithm, an improved adaptive line-of-sight (ALOS) guidance law is proposed, which is suitable for straight-line and curve paths. On the basis of guidance information provided by LOS, a three-degree-of-freedom (DOF) dynamic model of an underactuated USV has been used to design a practical path following controller. The controller is designed by combining backstepping method, neural shunting model, neural network minimum parameter learning method, and Nussbaum function. Neural shunting model is used to solve the problem of “explosion of complexity,” which is an inherent illness of backstepping algorithm. Meanwhile, a simpler neural network minimum parameter learning method than multilayer neural network is employed to identify the uncertainties and time-varying external disturbances. In particular, Nussbaum function is introduced into the controller design to solve the problem of unknown control gain coefficient. And much effort is made to obtain the stability for the closed-loop control system, using the Lyapunov stability theory. Simulation experiments demonstrate the effectiveness and reliability of the improved LOS guidance algorithm and the path following controller.


Solar Physics ◽  
2009 ◽  
Vol 258 (2) ◽  
pp. 297-318 ◽  
Author(s):  
Mohammadmahdi Rezaei Yousefi ◽  
Babak Salehi Kasmaei ◽  
Abdolhossein Vahabie ◽  
Caro Lucas ◽  
Babak Nadjar Araabi

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