Identification of Plasma Parameters and Optimization of Magnetic Sensors in the Superconducting Steady-State Tokamak-1 Using Neural Networks

2001 ◽  
Vol 39 (1) ◽  
pp. 1-17 ◽  
Author(s):  
A. Sengupta ◽  
P. Ranjan
Keyword(s):  
Neural Networks ◽  
Steady State ◽  
Magnetic Sensors ◽  
Plasma Parameters

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

Calibration of the ITER outer vessel steady-state magnetic sensors

2021 ◽  
Vol 168 ◽  
pp. 112398
Author(s):  
Slavomir Entler ◽  
Ivan Duran ◽  
Martin Kocan ◽  
George Vayakis ◽  
Petr Sladek ◽  
...  
Keyword(s):  
Steady State ◽  
Magnetic Sensors

Electron drift caused by rf field gradient creates many plasma phenomena: An attempt to distinguish the cause and the effect

2011 ◽  
Vol 78 (2) ◽  
pp. 165-174 ◽  
Author(s):  
C. L. XAPLANTERIS ◽  
E. D. FILIPPAKI ◽  
I. S. MISTAKIDIS ◽  
L. C. XAPLANTERIS
Keyword(s):  
Experimental Data ◽  
Steady State ◽  
Low Temperature ◽  
Mathematical Models ◽  
Field Gradient ◽  
Collision Frequency ◽  
The Other ◽  
Frequency Effect ◽  
Electron Drift ◽  
Plasma Parameters

AbstractMany experimental data along with their theoretical interpretations on the rf low-temperature cylindrical plasma have been issued until today. Our Laboratory has contributed to that research by publishing results and interpretative mathematical models. With the present paper, two issues are being examined; firstly, the estimation of electron drift caused by the rf field gradient, which is the initial reason for the plasma behaviour, and secondly, many new experimental results, especially the electron-neutral collision frequency effect on the other plasma parameters and quantities. Up till now, only the plasma steady state was taken into consideration when a theoretical elaboration was carried out, regardless of the cause and the effect. This indicates the plasma's complicated and chaotic configuration and the need to simplify the problem. In the present work, a classification about the causality of the phenomena is attempted; the rf field gradient electron drift is proved to be the initial cause.

Identification of a Steady-State Flow in Porous Media Using Artificial Neural Networks

2012 ◽  
Author(s):  
Marek J. Lefik ◽  
Daniela P. Boso ◽  
Bernhard A. Schrefler
Keyword(s):  
Neural Networks ◽  
Porous Media ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Concentration Field ◽  
Flow In Porous Media ◽  
Homogeneous Field ◽  
Steady State Flow ◽  
Convection Problem ◽  
Artificial Neural

For a steady state convection problem, assuming given concentration field values in a few measurement points and hydraulic head values in the same piezometers, the source of the concentration, and its intensity are deduced using Artificial Neural Networks (ANNs). ANNs are trained with data extracted from Finite Difference (FD) solution of a classical convection problem for small Peclet number. The numerical analysis is exemplified for vanishing, homogeneous and non-homogeneous field of velocity. It is shown that the diffusivity vector can also be identified. The complexity of the problem is discussed for each studied case.

An Estimation of 3-Way Catalyst Performance Using Artificial Neural Networks During Idle Speed

2004 ◽  
Author(s):  
P. N. Botsaris ◽  
D. Bechrakis ◽  
P. D. Sparis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Expert Knowledge ◽  
Operating Conditions ◽  
Experimental Results ◽  
Outlet Temperature ◽  
Practical Applications ◽  
Idle Speed ◽  
Artificial Neural

The intelligent control as fuzzy or artificial is based on either expert knowledge or experimental data and therefore it possesses intrinsic qualities like robustness and ease implementation. Lately, many researchers present studies aim to show that this kind of control can be used in practical applications such as the idle speed control problem in automotive industry. In this study, an estimation of an automobile three-way catalyst performance with artificial neural networks is presented. It may be an alternative approach for an on board diagnostic system (OBD) to predict the catalyst performance. This method was tested using data sets from two kind of catalysts, a brand new and an old one on a laboratory bench at idle speed. The catalyst operation during the “steady state” phase (the phase that the catalyst has reached its operating conditions and works normally) is examined. Further experiments are needed for different catalyst typed before the methods is proposed generally. It consists of 855 elements of catalyst inlet-outlet temperature difference (DT), hydrocarbons (HC), and carbon monoxide (CO) and carbon dioxide (CO2) emissions. The simulation: detects the values of HC, CO, CO2 using the DT as an input to our network forms a neural network. Results showed serious indications that artificial neural networks (or fuzzy logic control laws) could estimate the catalyst performance adequately depending their training process, if certain information about the catalyst system and the inputs and output of such system are known. In this study the “steady state” period experimental results are presented. In this paper the “steady state” period experimental results are presented.

SPATIAL DISORDER OF CNN — WITH ASYMMETRIC OUTPUT FUNCTION

2001 ◽  
Vol 11 (08) ◽  
pp. 2085-2095 ◽  
Author(s):  
JUNG-CHAO BAN ◽  
KAI-PING CHIEN ◽  
SONG-SUN LIN ◽  
CHENG-HSIUNG HSU
Keyword(s):  
Neural Networks ◽  
Steady State ◽  
Three Dimensional ◽  
Cellular Neural Networks ◽  
Output Function ◽  
One Dimensional ◽  
Spatial Entropy ◽  
The One ◽  
Steady State Solutions

This investigation will describe the spatial disorder of one-dimensional Cellular Neural Networks (CNN). The steady state solutions of the one-dimensional CNN can be replaced as an iteration map which is one dimensional under certain parameters. Then, the maps are chaotic and the spatial entropy of the steady state solutions is a three-dimensional devil-staircase like function.

Sign in / Sign up

Export Citation Format

Share Document