Improved adaptive neuro fuzzy inference system car-following behaviour model based on the driver–vehicle delay

2014 ◽  
Vol 8 (4) ◽  
pp. 323-332 ◽  
Author(s):  
Alireza Khodayari ◽  
Ali Ghaffari ◽  
Reinhard Braunstingl ◽  
Fatemeh Alimardani ◽  
Reza Kazemi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Car Following ◽  
Model Based ◽  
Neuro Fuzzy ◽  
Vehicle Delay

scholarly journals Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal

2017 ◽  
Vol 7 (7) ◽  
pp. 668 ◽  
Author(s):  
Moneer Faraj ◽  
Fahmi Samsuri ◽  
Ahmed Abdalla ◽  
Damhuji Rifai ◽  
Kharudin Ali
Keyword(s):  
Eddy Current ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
System Model ◽  
Current Signal ◽  
Inference System ◽  
Model Based ◽  
Neuro Fuzzy

scholarly journals A New ANFIS Model based on Multi-Input Hamacher T-norm and Subtract Clustering

2014 ◽  
Vol 8 (1) ◽  
pp. 833-838 ◽  
Author(s):  
Feng-Yi Zhang ◽  
Zhi-Gao Liao
Keyword(s):  
Empirical Study ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Anfis Model ◽  
Prediction Ability ◽  
Inference System ◽  
Model Based ◽  
Neuro Fuzzy ◽  
Proposed Model

This paper proposed a novel adaptive neuro-fuzzy inference system (ANFIS), which combines subtract clustering, employs adaptive Hamacher T-norm and improves the prediction ability of ANFIS. The expression of multiinput Hamacher T-norm and its relative feather has been originally given, which supports the operation of the proposed system. Empirical study has testified that the proposed model overweighs early work in the aspect of benchmark Box- Jenkins dataset and may provide a practical way to measure the importance of each rule.

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