scholarly journals Phonetic Classification by Adaptive Network Based Fuzzy Inference System and Subtractive Clustering

2014 ◽  
Author(s):  
Samiya Silarbi ◽  
Bendahmane Abderrahmane ◽  
Abdelkader Benyettou
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Subtractive Clustering ◽  
Adaptive Network ◽  
Inference System ◽  
Phonetic Classification

scholarly journals Prediction of Critical Factor of Safety in Soil Slopes Using Adaptive Network-Based Fuzzy Inference System (ANFIS)

2021 ◽  
Author(s):  
Babak
Keyword(s):  
Stability Analysis ◽  
Slope Stability ◽  
Fuzzy Inference System ◽  
Factor Of Safety ◽  
Fuzzy Inference ◽  
Critical Factor ◽  
Slope Stability Analysis ◽  
Subtractive Clustering ◽  
Adaptive Network ◽  
Inference System

Slope stability analysis is among the uncertain problems in geotechnical engineering; This is mostly because of the presence of the complex nature of the soil and rock mass and uncertain parameters affecting the problem.To Determine the structure of the model and deal with the complexity of the geotechnical behavior of soil a data approach is required.Previous studies in the literature do not predict slope stability with high accuracy.In this study, to increase the accuracy of prediction of slope stability, the Adaptive Network-Based Fuzzy Inference System (ANFIS) was used. Three clustering methods are used in combination with ANFIS, namely the Grid Partitioning (GP) method, Subtractive Clustering Method (SCM), and Fuzzy C-means Method (FCM) for predicting the critical Factor of Safety (FS). Since the coefficient of determination R^(2 )in the SCM model is closer to 1, the prediction performance of the SCM model outperforms the GP and FCM. The results of the study indicate that ANFIS can provide more accurate predictions compared to other conventional slope stability analysis approaches. ANFIS method efficiently reduced the uncertainties involved in the estimation and analysis of slope stability as compared to previous approaches in the literature.

Virtual fit evaluation of pants using the Adaptive Network Fuzzy Inference System

2021 ◽  
pp. 004051752110205
Author(s):  
Xueqing Zhao ◽  
Ke Fan ◽  
Xin Shi ◽  
Kaixuan Liu
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Test Accuracy ◽  
Adaptive Network ◽  
Data Set ◽  
Inference System ◽  
Second Stage ◽  
Simulated Environment ◽  
Two Stages ◽  
Fold Cross Validation

Virtual reality is a technology that allows users to completely interact with a computer-simulated environment, and put on new clothes to check the effect without taking off their clothes. In this paper, a virtual fit evaluation of pants using the Adaptive Network Fuzzy Inference System (ANFIS), VFE-ANFIS for short, is proposed. There are two stages of the VFE-ANFIS: training and evaluation. In the first stage, we trained some key pressure parameters by using the VFE-ANFIS; these key pressure parameters were collected from real try-on and virtual try-on of pants by users. In the second stage, we evaluated the fit by using the trained VFE-ANFIS, in which some key pressure parameters of pants from a new user were determined and we output the evaluation results, fit or unfit. In addition, considering the small number of input samples, we used the 10-fold cross-validation method to divide the data set into a training set and a testing set; the test accuracy of the VFE-ANFIS was 94.69% ± 2.4%, and the experimental results show that our proposed VFE-ANFIS could be applied to the virtual fit evaluation of pants.

Single Input Multi Output Adaptive Network Based Fuzzy Inference System for Machinability Data Selection in Turning Operations

2011 ◽  
Vol 383-390 ◽  
pp. 1062-1070
Author(s):  
Adeel H. Suhail ◽  
N. Ismail ◽  
S.V. Wong ◽  
N.A. Abdul Jalil
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cutting Parameters ◽  
Machining Process ◽  
Data Selection ◽  
Adaptive Network ◽  
Inference System ◽  
Single Input ◽  
Machinability Data

The selection of machining parameters needs to be automated, according to its important role in machining process. This paper proposes a method for cutting parameters selection by fuzzy inference system generated using fuzzy subtractive clustering method (FSCM) and trained using an adaptive network based fuzzy inference system (ANFIS). The desired surface roughness (Ra) was entered into the first step as a reference value for three fuzzy inference system (FIS). Each system determine the corresponding cutting parameters such as (cutting speed, feed rate, and depth of cut). The interaction between these cutting parameters were examined using new sets of FIS models generated and trained for verification purpose. A new surface roughness value was determined using the cutting parameters resulted from the first steps and fed back to the comparison unit and was compared with the desired surface roughness and the optimal cutting parameters ( which give the minimum difference between the actual and predicted surface roughness were find out). In this way, single input multi output ANFIS architecture presented which can identify the cutting parameters accurately once the desired surface roughness is entered to the system. The test results showed that the proposed model can be used successfully for machinability data selection and surface roughness prediction as well.

scholarly journals Prediction of Blast-Induced Ground Vibration Using an Adaptive Network-Based Fuzzy Inference System

2020 ◽  
Vol 11 (1) ◽  
pp. 203
Author(s):  
Primož Jelušič ◽  
Andrej Ivanič ◽  
Samo Lubej
Keyword(s):  
Fuzzy Inference System ◽  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Ground Vibration ◽  
Residential Building ◽  
Ground Vibrations ◽  
Adaptive Network ◽  
Inference System ◽  
Fast Learning

Efforts were made to predict and evaluate blast-induced ground vibrations and frequencies using an adaptive network-based fuzzy inference system (ANFIS), which has a fast-learning capability and the ability to capture the non-linear response during the blasting process. For this purpose, the ground vibrations generated by the blast in a tunnel tube were monitored at a residential building located directly above the tunnel tube. To investigate the usefulness of this approach, the prediction by the ANFIS was also compared to those by three of the most commonly used vibration predictors. The efficiency criteria chosen for the comparison between the predicted and actual data were the sum of squares due to error (SSE), the root mean squared error (RMSE), and the goodness of fit (R-squared and adjusted R-squared). The results show that the ANFIS prediction model performs better than the commonly used predictors.

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