Airworthiness Evaluation Model Based on Fuzzy Neural Network

2019 ◽  
pp. 183-194
Author(s):  
Jie-Ru Jin ◽  
Peng Wang ◽  
Yang Shen ◽  
Kai-Xi Zhang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Model Based ◽  
Fuzzy Neural

Study on Intelligent Analysis of the Causes of SMT Solder Joint Defects Based on Fuzzy Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 3257-3261
Author(s):  
Chun Yue Huang ◽  
He Geng Wei ◽  
Tian Ming Li ◽  
De Jin Yan
Keyword(s):  
Neural Network ◽  
Solder Joint ◽  
Membership Function ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Training Samples ◽  
Intelligent Analysis ◽  
Fuzzy Neural ◽  
Input Variables ◽  
Set Up

By determining membership function of the input parameters and selecting defuzzification method, the evaluation model which can be used to intelligent analyzing the causes of SMT solder joint defects was set up. The fuzzy neural network was trained by using the output variables of the training samples from intelligent discrimination as the input variables of training samples of fuzzy neural network. The fuzzy neural network was tested by using the output variables of the testing samples from intelligent discrimination as the input variables of testing samples of fuzzy neural network. The results show that by using the evaluation model the cause of SMT solder joint defects can be analyzed intelligently and the results of intelligently analysis are reasonable, the evaluation model can be used practically.

Application of T-S Fuzzy Neural Network in the Assessment of River Ecosystem Health

2013 ◽  
Vol 726-731 ◽  
pp. 958-962 ◽  
Author(s):  
Zhen Chun Hao ◽  
Xiao Li Liu ◽  
Qin Ju
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy System ◽  
Ecosystem Health ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Fuzzy Model ◽  
River Ecosystem ◽  
Fuzzy Neural

Healthy river ecosystem has been acknowledged as the object of river management, which is crucial for the sustainable development of cities. Simple and practical evaluation methods with great precision are necessary for the evaluation of river ecosystem health. Fuzzy system has been widely used in evaluation and decision making for its simple reasoning and the adoption of experts knowledge. However, much artificial intervention decreases the precision. Neural network has a strong ability of self-leaning while it is not good at expressing rule-based knowledge. The T-S fuzzy neural network model combines the advantages of fuzzy system and neural network. In this paper, the T-S fuzzy neural network model was used to establish a river ecosystem health evaluation model. Results show that the combination of T-S fuzzy model and neural network eliminates the influences of subjective factors and improve the final precisions efficiently.

Chooseing Suitable BIM Software for Engineering Projects Based on the Fuzzy Neural Network Evaluation Model

2013 ◽  
Vol 405-408 ◽  
pp. 3348-3351 ◽  
Author(s):  
Jian Feng ◽  
Jian Ping Zhang ◽  
Shun Ping Gao
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Technology Development ◽  
Evaluation Model ◽  
Case Analysis ◽  
Chinese Market ◽  
Fuzzy Neural ◽  
Network Evaluation ◽  
Set Up ◽  
Engineering Projects

With BIM technology development in the Chinese market, there are many different BIM software invented. How according to the engineering characteristics and needs to select the appropriate software in numerous BIM products, will be save a lot of manpower and resources. These paper will based on literature research and case analysis, find out the appropriate project between BIM software and Engineering; Making up the adaptable index system by statistical analysis; Finally set up the fuzzy neural network evaluation model. Effectively to provide decision support information by this model for choosing suitable for BIM software.

scholarly journals Study of the suitable water-saving technique model based on fuzzy neural network for Wannian County

2011 ◽  
Vol 15 ◽  
pp. 4362-4366 ◽  
Author(s):  
Huang Binbin ◽  
Gui Faliang ◽  
Zhang Xiaohui
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Water Saving ◽  
Model Based ◽  
Fuzzy Neural
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