Data rectification and gross error detection in a steady state process via artificial neural networks

1993 ◽  
Vol 32 (12) ◽  
pp. 3020-3028 ◽  
Author(s):  
Patricia A. Terry ◽  
David M. Himmelblau
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Error Detection ◽  
Gross Error Detection ◽  
Data Rectification ◽  
State Process ◽  
Gross Error ◽  
Artificial Neural ◽  
Steady State Process

Application of neural networks for gross error detection

1993 ◽  
Vol 32 (8) ◽  
pp. 1651-1657 ◽  
Author(s):  
Gagan Gupta ◽  
Shankar Narasimhan
Keyword(s):  
Neural Networks ◽  
Error Detection ◽  
Gross Error Detection ◽  
Gross Error

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.

Functional Approximation of Power System Steady-State Voltage Stability Limits by Artificial Neural Networks

2013 ◽  
Vol 24 (4) ◽  
pp. 544-554 ◽  
Author(s):  
Guilherme G. Lage ◽  
Ricardo A. S. Fernandes ◽  
Geraldo R. M. da Costa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Power System ◽  
Voltage Stability ◽  
Functional Approximation ◽  
Artificial Neural ◽  
Stability Limits
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