Interaction Between Hydraulic Fracture and Natural Fracture - A New Prediction Model by Adaptive Neuro-Fuzzy Inference System (ANFIS)

2014 ◽  
Author(s):  
Peng Chen ◽  
M Motiur Rahman ◽  
Hemanta Kumar Sarma
Keyword(s):  
Prediction Model ◽  
Hydraulic Fracture ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Natural Fracture ◽  
Inference System ◽  
Neuro Fuzzy

Thermal Error Prediction of the Spindle Using Adaptive Neuro-Fuzzy Inference System

2015 ◽  
Vol 789-790 ◽  
pp. 263-267
Author(s):  
Yan Lei Li ◽  
Ming Yan Wang ◽  
You Min Hu ◽  
Bo Wu
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Thermal Error ◽  
Temperature Data ◽  
Current Sensor ◽  
Error Prediction ◽  
Inference System ◽  
Neuro Fuzzy

This paper proposes a new method to predict the spindle deformation based on temperature data. The method introduces ANFIS (adaptive neuro-fuzzy inference system). For building the predictive model, we first extract temperature data from sensors in the spindle, and then they are used as the inputs to train ANFIS. To evaluate the performance of the prediction, an experiment is implemented. Three Pt-100 thermal resistances is used to monitor the spindle temperature, and an inductive current sensor is used to obtain the spindle deformation. The experimental results display that our prediction model can better predict the spindle deformation and improve the performance of the spindle.

Bus service quality prediction and attribute ranking using probabilistic neural network and adaptive neuro fuzzy inference system

2016 ◽  
Vol 43 (9) ◽  
pp. 822-829 ◽  
Author(s):  
Rokibul Islam ◽  
Sarder Rafee Musabbir ◽  
Irfan Uddin Ahmed ◽  
Md. Hadiuzzaman ◽  
Mehedi Hasnat ◽  
...  
Keyword(s):  
Neural Network ◽  
Service Quality ◽  
Prediction Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Probabilistic Neural Network ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Bus Service

This study applies probabilistic neural network (PNN) and adaptive neuro fuzzy inference system (ANFIS) to develop bus service quality (SQ) prediction model based on the preferences stated by users (on a scale of 1 to 5). A questionnaire survey is conducted and a data set from the survey is prepared to develop the SQ prediction model using PNN and ANFIS. Results show that ANFIS produced better prediction than PNN. The research is further extended to include ranking of the SQ attributes according to their impact on the overall result from the developed model. Attributes such as punctuality and reliability, seat availability, and service frequency were found to be the top three attributes that mostly affect the decision making process of the users. This study can aid service providers in improving the most important attributes of bus service to develop the quality of service, thereby increasing transit ridership.

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