Study on the Social Influence Evaluation of the Urban Old Industrial Zones’ Upgrade Projects Based on Fuzzy Neural Network Method

2013 ◽  
pp. 695-704
Author(s):  
Nan Jia ◽  
Xing Bi
Keyword(s):  
Neural Network ◽  
Social Influence ◽  
Fuzzy Neural Network ◽  
Neural Network Method ◽  
The Social ◽  
Network Method ◽  
Fuzzy Neural ◽  
Industrial Zones

Performance Prediction of Pre-Corroded Aluminum Alloy Using Genetic Algorithm-Neural Network and Fuzzy Neural Network

2008 ◽  
Vol 33-37 ◽  
pp. 1283-1288 ◽  
Author(s):  
Chao Hua Fan ◽  
Yu Ting He ◽  
Hong Peng Li ◽  
Feng Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Aluminum Alloy ◽  
Aluminum Alloys ◽  
Performance Prediction ◽  
Bp Neural Network ◽  
Fuzzy Neural Network ◽  
Neural Network Method ◽  
Network Method ◽  
Fuzzy Neural

Genetic algorithm is introduced in the study of network authority values of BP neural network, and a GA-NN algorithm is established. Based on this genetic algorithm-neural network method, a predictive model for fatigue performances of the pre-corroded aluminum alloys under a varied corrosion environmental spectrum was developed by means of training from the testing dada. At the same time, a fuzzy-neural network method is established for the same purpose. The results indicate that genetic algorithm-neural network and fuzzy-neural network can both be employed to predict the underlying fatigue performances of the pre-corroded aluminum alloy precisely.

Transient probabilistic design of flexible multibody system using a dynamic fuzzy neural network method with distributed collaborative strategy

2018 ◽  
Vol 233 (11) ◽  
pp. 4077-4090 ◽  
Author(s):  
Lu-Kai Song ◽  
Guang-Chen Bai ◽  
Cheng-Wei Fei ◽  
Rhea P Liem
Keyword(s):  
Neural Network ◽  
Probabilistic Analysis ◽  
Multibody Systems ◽  
Fuzzy Neural Network ◽  
Robot Manipulator ◽  
Neural Network Method ◽  
Surface Method ◽  
Network Method ◽  
Flexible Multibody ◽  
Fuzzy Neural

To improve the efficiency and accuracy of transient probabilistic analysis of flexible multibody systems, a dynamic fuzzy neural network method-based distributed collaborative strategy is proposed by integrating extremum response surface method and fuzzy neural network. Distributed collaborative dynamic fuzzy neural network method is mathematically modeled and derived by considering the high nonlinearity, strong coupling, and multicomponent characteristics of a flexible multibody system. The proposed method is demonstrated to perform the transient probabilistic analysis of a two-link flexible robot manipulator. We obtain the distributional characteristics, reliability degree, and sensitivity degree of robot manipulator, which are useful for the effective design of robot manipulator. By comparing the full-scale method, extremum response surface method, dynamic fuzzy neural network method, and distributed collaborative dynamic fuzzy neural network method, we find that the distributed collaborative dynamic fuzzy neural network method can be used to perform the transient probabilistic analysis of the robot manipulator and improve the computational efficiency while maintaining a good accuracy. Moreover, this study offers a useful insight for the reliability-based design optimization of flexible multibody systems, and enriches the field of mechanical reliability theory as well.

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.

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