scholarly journals The Impact of Sampling and Rule Set Size on Generated Fuzzy Inference System Predictive Accuracy: Analysis of a Software Engineering Data Set

2011 ◽  
pp. 360-369
Author(s):  
Stephen G. MacDonell
Keyword(s):  
Software Engineering ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Predictive Accuracy ◽  
Accuracy Analysis ◽  
Data Set ◽  
Inference System ◽  
Set Size ◽  
Rule Set ◽  
The Impact

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.

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.

Rule-based fuzzy inference system for estimating the influent COD/N ratio and ammonia load to a sequencing batch reactor

2006 ◽  
Vol 53 (1) ◽  
pp. 199-207 ◽  
Author(s):  
Y.J. Kim ◽  
H. Bae ◽  
J.H. Ko ◽  
K.M. Poo ◽  
S. Kim ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Sequencing Batch Reactor ◽  
Fuzzy Inference ◽  
Batch Reactor ◽  
Rule Generation ◽  
Decision Tree Algorithm ◽  
Inference System ◽  
Rule Based ◽  
Close Relationship ◽  
Rule Set

A fuzzy inference system using sensor measurements was developed to estimate the influent COD/N ratio and ammonia load. The sensors measured ORP, DO and pH. The sensor profiles had a close relationship with the influent COD/N ratio and ammonia load. To confirm this operational knowledge for constructing a rule set, a correlation analysis was conducted. The results showed that a rule generation method based only on operational knowledge did not generate a sufficiently accurate relationship between sensor measurements and target variables. To compensate for this defect, a decision tree algorithm was used as a standardized method for rule generation. Given a set of inputs, this algorithm was used to determine the output variables. However, the generated rules could not estimate the continuous influent COD/N ratio and ammonia load. Fuzzified rules and the fuzzy inference system were developed to overcome this problem. The fuzzy inference system estimated the influent COD/N ratio and ammonia load quite well. When these results were compared to the results from a predictive polynomial neural network model, the fuzzy inference system was more stable.

Fault Diagnosis of Roller Bearing Conditions Using ANFIS

2009 ◽  
Vol 16-19 ◽  
pp. 886-890 ◽  
Author(s):  
Wen Tao Sui ◽  
Dan Zhang
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Roller Bearing ◽  
Back Propagation ◽  
Selection Criterion ◽  
Data Set ◽  
Inference System ◽  
Roller Bearings

This paper presents a fault diagnosis method on roller bearings based on adaptive neuro-fuzzy inference system (ANFIS) in combination with feature selection. The class separability index was used as a feature selection criterion to select pertinent features from data set. An adaptive neural-fuzzy inference system was trained and used as a diagnostic classifier. For comparison purposes, the back propagation neural networks (BPN) method was also investigated. The results indicate that the ANFIS model has potential for fault diagnosis of roller bearings.

scholarly journals Measuring Software Quality Product Based on Fuzzy Inference System Techniques in ISO Standard

2021 ◽  
Author(s):  
Atrin Barzegar
Keyword(s):  
Software Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Model ◽  
Inference System ◽  
Iso Standard ◽  
Software Products ◽  
Software Product ◽  
The Impact

The success of a software product depends on several factors. Given that different organizations and institutions use software products, the need to have a quality and desirable software according to the goals and needs of the organization makes measuring the quality of software products an important issue for most organizations and institutions. To be sure of having the right software. It is necessary to use a standard quality model to examine the features and sub-features for a detailed and principled study in the quality discussion. In this study, the quality of Word software was measured. Considering the importance of software quality and to have a good and usable software in terms of quality and measuring the quality of software during the study, experts and skilled in this field were used and the impact of each factor and quality characteristics. It was applied at different levels according to their opinion to make the result of measuring the quality of Word software more accurate and closer to reality. In this research, the quality of the software product is measured based on the fuzzy inference system in ISO standard. According to the results obtained in this study, it is understood that quality is a continuous and hierarchical concept and the quality of each part of the software at any stage of production can lead to high quality products.

scholarly journals Modelling Unconfined Groundwater Recharge Using Adaptive Neuro-Fuzzy Inference System

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1280
Author(s):  
Khaled Mohamed Nabil I. Elsayed ◽  
Rabee Rustum ◽  
Adebayo J. Adeloye
Keyword(s):  
Groundwater Recharge ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Monitoring Site ◽  
Data Set ◽  
Inference System ◽  
The North ◽  
Neuro Fuzzy ◽  
Ground Water Recharge ◽  
Human Thinking

Estimating groundwater recharge using mathematical models such as water budget or soil water balance method has been proved to be very difficult due to the complex, uncertain multidimensional nature of the process, despite the simplicity of the concept. Artificial Intelligence (AI) techniques have been proposed to deal with this complexity and uncertainty in a similar way to human thinking and reasoning. This study proposed the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to model unconfined groundwater recharge using a set of data records from Kaharoa monitoring site in the North Island of New Zealand. Fifty-three data points, comprising a set of input parameters such as rainfall, temperature, sunshine hours, and radiation, for a period of approximately four and a half years, have been used to estimate ground water recharge. The results suggest that the ANFIS model is overall a reliable estimator for groundwater recharge, the correlation coefficient of the model reached 93% using independent data set. The method is easy, flexible and reliable; hence, it is recommended to be used for similar applications.

Application of fuzzy inference system for analysis of oil field data to optimize combustion engine maintenance

2019 ◽  
Vol 233 (14) ◽  
pp. 3736-3745
Author(s):  
David Vališ ◽  
Libor Žák ◽  
Zdenek Vintr
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Combustion Engine ◽  
Remaining Useful Life ◽  
Oil Field ◽  
Calendar Time ◽  
Data Set ◽  
Inference System ◽  
Diagnostic Data ◽  
Diagnostic Measures

The condition of a technical system has been subject to intense scrutiny in recent years. Monitoring the technical condition of a system may be performed by applying different approaches. The main intention of the monitoring is to get the information about the instant system condition, and to estimate and predict reliability measures. In the article, the authors suggest possible ways to process diagnostic measures which have the potential to determine the system condition and to predict its future development. The diagnostic measures are in this case indirect and they are introduced in the form of oil data. The diagnostic data are obtained from the tribodiagnostic system which is composed of kinematic pairs and oil. The analysed oil samples come from the combustion engine of a heavy ground vehicle. The authors focus on the output values in the form of wear particles, iron and lead, and additive particles. The concentration of these particles in the oil is influenced by operating time and calendar time. However, the particles include inherent and natural levels of uncertainty and fuzziness. Therefore, the authors apply and present the models imitating the development of the particles which are based on a fuzzy inference system. Highly valuable and extensive data set records enabled the authors to perform two-dimensional data modelling based both on operation time and calendar time. The obtained results enable us to predict the remaining useful life of the system. Moreover, the results could also be beneficial when modifying hard time scheduled preventive maintenance intervals (e.g. when to change the oil). The major contribution of this paper is the fact that all analysed diagnostic data are not artificial but real; moreover, they were collected for more than 10 years and therefore contain hundreds of records.

The Adaptive Neuro-Fuzzy Inference System (ANFIS) Application for the Ammonium Removal from Aqueous Solution Predicting by Biochar

2018 ◽  
Vol 931 ◽  
pp. 985-990
Author(s):  
Ahmed S. Khalil ◽  
Sergey V. Starovoytov ◽  
Nikolai S. Serpokrylov
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Pilot Scale ◽  
Fish Farms ◽  
Anfis Model ◽  
Data Set ◽  
Inference System ◽  
Ammonium Removal ◽  
Neuro Fuzzy

The adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the removal of ammonium () from wastewater. The ANFIS model was developed and validated with a data set from a pilot-scale of adsorption system treating aqueous solutions and wastewater from fish farms. The data sets consist of four parameters, which include pH, temperature, an initial concentration of ammonium and amount of adsorbent. The adsorbent was biochar obtained from rice straw. The ANFIS models performance was assessed through the root mean absolute error (RMSE) and was validated by testing data. The results of the study show that the adaptive neuro-fuzzy inference system (ANFIS) is able to predict the percentage of ammonium removal from adsorption column according to the input variables with acceptable accuracy, suggesting that the adaptive neuro-fuzzy inference system model is a valuable tool for estimating the quality of fish farms water. This model of ANFIS leads to cost reduction because prediction can be done without resorting to efforts that require cost and time.

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