Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution

2018 ◽  
Vol 621 ◽  
pp. 1124-1141 ◽  
Author(s):  
Haoyuan Hong ◽  
Mahdi Panahi ◽  
Ataollah Shirzadi ◽  
Tianwu Ma ◽  
Junzhi Liu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Susceptibility Assessment ◽  
Inference System ◽  
Flood Susceptibility ◽  
Neuro Fuzzy

scholarly journals Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm

2019 ◽  
Vol 9 (4) ◽  
pp. 780 ◽  
Author(s):  
Khalid Elbaz ◽  
Shui-Long Shen ◽  
Annan Zhou ◽  
Da-Jun Yuan ◽  
Ye-Shuang Xu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pressure Balance ◽  
Anfis Model ◽  
Inference System ◽  
Advance Rate ◽  
Multi Objective ◽  
Neuro Fuzzy ◽  
Epb Shield

The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field.

Modeling of rheological behavior of honey using genetic algorithm–artificial neural network and adaptive neuro-fuzzy inference system

2015 ◽  
Vol 9 ◽  
pp. 60-67 ◽  
Author(s):  
Marziyeh Ramzi ◽  
Mahdi Kashaninejad ◽  
Fakhreddin Salehi ◽  
Ali Reza Sadeghi Mahoonak ◽  
Seyed Mohammad Ali Razavi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Rheological Behavior ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural
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