Neural network approach for mobile bay water quality mapping with spaceborne measurements

2009 ◽  
Author(s):  
He Yang ◽  
Qian Du
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Mobile Bay ◽  
Network Approach ◽  
Neural Network Approach ◽  
Water Quality Mapping

scholarly journals A Neural Network Approach to Smarter Sensor Networks for Water Quality Monitoring

Sensors ◽  
2012 ◽  
Vol 12 (4) ◽  
pp. 4605-4632 ◽  
Author(s):  
Edel O’Connor ◽  
Alan F. Smeaton ◽  
Noel E. O’Connor ◽  
Fiona Regan
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Sensor Networks ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Network Approach ◽  
Neural Network Approach

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

A neural network approach to anomaly detection in spectra

1997 ◽  
Author(s):  
Daniel Benzing ◽  
Kevin Whitaker ◽  
Dedra Moore ◽  
Daniel Benzing ◽  
Kevin Whitaker ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach
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