scholarly journals New fuzzy neural network–Markov model and application in mid- to long-term runoff forecast

2016 ◽  
Vol 61 (6) ◽  
pp. 1157-1169 ◽  
Author(s):  
Biao Shi ◽  
Chang Hua Hu ◽  
Xin Hua Yu ◽  
Xiao Xiang Hu
Aviation ◽  
2013 ◽  
Vol 17 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Tatiana Tseytlina ◽  
Victor Balashov ◽  
Andrey Smirnov

In this work we developed a fuzzy neural network-based model of the conditions for the existence of air routes, i.e. the rules underlying the emergence, existence and elimination of air routes (direct links between cities). The model belongs to the class of information models: the existence or non-existence of an air route is considered dependent on a complex of parameters. These parameters characterise the transport link, as well as the generational and target capabilities of the connected cities. The model was constructed using genetic algorithm techniques and self-organising Kohonen maps (implemented by software features of the STATISTICA package), as well as software tools of the Fuzzy Logic Toolbox and the Neural Network Toolbox of the MatLab development environment. The model is used to forecast the development of the topology of the network. The forecast is a necessary component of long-term forecasts of demand in the aircraft market.


2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
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.


2010 ◽  
Vol 36 (3) ◽  
pp. 459-464 ◽  
Author(s):  
Cheng-Dong LI ◽  
Jian-Qiang YI ◽  
Yi YU ◽  
Dong-Bin ZHAO

Sign in / Sign up

Export Citation Format

Share Document