A neural network approach to identifying non-point sources of microbial contamination

1999 ◽  
Vol 33 (14) ◽  
pp. 3099-3106 ◽  
Gail Montgomery Brion ◽  
Srinivasa Lingireddy
2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Bendaoud Mebarek ◽  
Mourad Keddam

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.

Ergonomics ◽  
2015 ◽  
Vol 59 (2) ◽  
pp. 207-221 ◽  
Felix Ladstätter ◽  
Eva Garrosa ◽  
Bernardo Moreno-Jiménez ◽  
Vicente Ponsoda ◽  
José Manuel Reales Aviles ◽  

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