scholarly journals Exploring the Influence of Truck Proportion on Freeway Traffic Safety Using Adaptive Network-Based Fuzzy Inference System

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Shiwen Zhang ◽  
Yingying Xing ◽  
Jian Lu ◽  
H. Michael Zhang
Keyword(s):  
Traffic Safety ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Traffic Volume ◽  
Learning Ability ◽  
Adaptive Network ◽  
Freeway Traffic ◽  
Inference System ◽  
Traffic Conflicts ◽  
The Impact

The truck operation of freeway has an impact on traffic safety. In particular, the gradually increasing in truck proportion will inevitably affect the freeway traffic operation of different traffic volume. In this paper, VISSIM simulation is used to supply the field data and orthogonal experimental is designed for calibrate the simulation data. Then, SSAM modeling is combined to analyze the impact of truck proportion on traffic flow parameters and traffic conflicts. The serious and general conflict prediction model based on the Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to determine the impact of the truck proportion on freeway traffic safety. The results show that when the truck proportion is around 0.4 under 3200 veh/h and 0.6 under 2600 veh/h, there are more traffic conflicts and the number of serious conflicts is more than the number of general conflicts, which also reflect the relationship between truck proportion and traffic safety. Under 3000 veh/h, travel time and average delay increasing while mean speed and mean speed of small car decreases with truck proportion increases. The mean time headway rises largely with the truck proportion increasing above 3000 veh/h. The speed standard deviation increases initially and then fall with truck proportion increasing. The lane-changing decreases while truck proportion increasing. In addition, ANFIS can accurately determine the impact of truck proportion on traffic conflicts under different traffic volume, and also validate the learning ability of ANFIS.

scholarly journals Using an Adaptive Network-based Fuzzy Inference System to Estimate the Vertical Force in Single Point Incremental Forming

2019 ◽  
Vol 14 (1) ◽  
pp. 63-77 ◽  
Author(s):  
Sever Gabriel Racz ◽  
Radu Eugen Breaz ◽  
Octavian Bologa ◽  
Melania Tera ◽  
Valentin Stefan Oleksik
Keyword(s):  
Experimental Data ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Incremental Forming ◽  
Learning Ability ◽  
Manufacturing Processes ◽  
Vertical Force ◽  
Adaptive Network ◽  
Inference System ◽  
Incremental Step

Manufacturing processes are usually complex ones, involving a significant number of parameters. Unconventional manufacturing processes, such as incremental forming is even more complex, and the establishment of some analytical relationships between parameters is difficult, largely due to the nonlinearities in the process. To overcome this drawback, artificial intelligence techniques were used to build empirical models from experimental data sets acquired from the manufacturing processes. The approach proposed in this work used an adaptive network-based fuzzy inference system to extract the value of technological force on Z-axis, which appears during incremental forming, considering a set of technological parameters (diameter of the tool, feed and incremental step) as inputs. Sets of experimental data were generated and processed by means of the proposed system, to make use of the learning ability of it to extract the empirical values of the technological force from rough data.

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.

ANFIS Model for Time Series Prediction

2013 ◽  
Vol 385-386 ◽  
pp. 1411-1414 ◽  
Author(s):  
Xue Bo Jin ◽  
Jiang Feng Wang ◽  
Hui Yan Zhang ◽  
Li Hong Cao
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Prediction Error ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Prediction Performance ◽  
Membership Functions ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System

This paper describes an architecture of ANFIS (adaptive network based fuzzy inference system), to the prediction of chaotic time series, where the goal is to minimize the prediction error. We consider the stock data as the time series. This paper focuses on how the stock data affect the prediction performance. In the experiments we changed the number of data as input of the ANFIS model, the type of membership functions and the desired goal error, thereby increasing the complexity of the training.

Identification and Online Validation of a pH Neutralization Process Using an Adaptive Network-Based Fuzzy Inference System

2015 ◽  
Vol 203 (4) ◽  
pp. 516-526 ◽  
Author(s):  
Ariane Silva Mota ◽  
Mauro Renault Menezes ◽  
Jones Erni Schmitz ◽  
Thiago Vaz da Costa ◽  
Flávio Vasconcelos da Silva ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Adaptive Network ◽  
Inference System ◽  
Neutralization Process ◽  
Ph Neutralization ◽  
Ph Neutralization Process
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