Reduction and control of flux decline in cross-flow membrane processes modeled by artificial neural networks

2006 ◽  
Vol 286 (1-2) ◽  
pp. 125-132 ◽  
Author(s):  
Stefano Curcio ◽  
Vincenza Calabrò ◽  
Gabriele Iorio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cross Flow ◽  
Membrane Processes ◽  
Flux Decline ◽  
Artificial Neural ◽  
And Control

Predicting the flux decline in milk cross-flow ceramic ultrafiltration by artificial neural networks

Desalination ◽  
2010 ◽  
Vol 250 (3) ◽  
pp. 1118-1120 ◽  
Author(s):  
Antonio Guadix ◽  
Jose E. Zapata ◽  
M. Carmen Almecija ◽  
Emilia M. Guadix
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cross Flow ◽  
Flux Decline ◽  
Artificial Neural

Artificial Neural Networks vs. Fuzzy Logic: Simple Tools to Predict and Control Complex Processes—Application to Plasma Spray Processes

2008 ◽  
Vol 17 (3) ◽  
pp. 365-376 ◽  
Author(s):  
Abdoul-Fatah Kanta ◽  
Ghislain Montavon ◽  
Michel Vardelle ◽  
Marie-Pierre Planche ◽  
Christopher C. Berndt ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Plasma Spray ◽  
Complex Processes ◽  
Artificial Neural ◽  
And Control ◽  
Control Complex

Einsatz von künstlichen neuronalen Netzwerken an Scannersystemen*/Application of artificial neural networks on scanner systems

2017 ◽  
Vol 107 (07-08) ◽  
pp. 536-540
Author(s):  
S. J. Pieczona ◽  
F. Muratore ◽  
M. F. Prof. Zäh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
And Control ◽  
Dynamic Enhancement

Zur Dynamiksteigerung von Scannersystemen werden verschiedene Arten von Modellierungs- und Regelungsmethoden in der Forschung genutzt. Jedoch sind Nichtlinearitäten, welche das Systemverhalten nachweisbar beeinflussen, in aller Regel nicht Teil der Untersuchung. Mit der Anwendung künstlicher neuronaler Netzwerke (KNN) wird das gesamte dynamische Systemverhalten sowohl für ein geregeltes als auch für ein ungeregeltes Scannersystem abgebildet. So wird geklärt, ob sich diese Art der Modellbildung für eine zukünftige Dynamiksteigerung eignet.   To enhance the dynamics of a scanner system, different methods of modelling and control are utilized. Nonlinearities, which have a certain impact on the system’s behavior, are generally ignored, though. By applying artificial neural networks, the overall dynamics of a controlled and an uncontrolled scanner could be represented. Thus, it will be clarified whether this kind of modelling is appropriate for a future dynamic enhancement.

A Neural Network-Based Approach for Pest Detection and Control in Modern Agriculture Using Internet of Things

2021 ◽  
pp. 1-31
Author(s):  
Pankaj Dadheech ◽  
Ankit Kumar ◽  
Vijander Singh ◽  
Linesh Raja ◽  
Ramesh C. Poonia
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neural Structure ◽  
Modern Agriculture ◽  
Different Types ◽  
Pest Detection ◽  
Real Time Applications ◽  
Artificial Neural ◽  
And Control ◽  
The Brain

The networks acquire an altered move towards the difficulty solving skills rather than that of conventional computers. Artificial neural networks are comparatively crude electronic designs based on the neural structure of the brain. The chapter describes two different types of approaches to training, supervised and unsupervised, as well as the real-time applications of artificial neural networks. Based on the character of the application and the power of the internal data patterns we can normally foresee a network to train quite well. ANNs offers an analytical solution to conventional techniques that are often restricted by severe presumptions of normality, linearity, variable independence, etc. The chapter describes the necessities of items required for pest management through pheromones such as different types of pest are explained and also focused on use of pest control pheromones.

Weld modeling and control using artificial neural networks

1995 ◽  
Vol 31 (6) ◽  
pp. 1484-1491 ◽  
Author(s):  
G.E. Cook ◽  
R.J. Barnett ◽  
K. Andersen ◽  
A.M. Strauss
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modeling And Control ◽  
Artificial Neural ◽  
And Control
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