Modeling of the Safety Climate and the Cultural Attitudes to Predict Unsafe Behaviors Using the Neuro-Fuzzy Inference System (ANFIS)

Background: Unsafe behavior in industries can be due to different factors. The aim of this study was to predict and model unsafe behavior using a safety atmosphere and cultural attitudes questionnaires. Methods: This study was a descriptive-analytic and cross-sectional examination that analyzed the data and predicted the unsafe behaviors of 90 construction workers using Neuro-Fuzzy Inference System (ANFIS) in MATLAB R2016a software. Results: In this study, the model of the safety atmosphere - unsafe behavior and the model of the cultural attitudes - unsafe behavior had the regression coefficients of 0.93373 and 0.9234, respectively. It showed that each of the parameters has a close relationship to the rate of the unsafe behavior. In this regard, a combination of the safety atmosphere and safety attitude parameters for the estimation of the unsafe behaviors achieved the better results with a regression coefficient of 0.9453 which indicates the direct effect of both parameters simultaneously on unsafe behavior. Conclusion: Based on the findings, it can be concluded that the neuro-fuzzy model can be used as an appropriate tool for predicting unsafe behavior in the industries.

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Research of Faulty Sensors Data Reconstructed for Liquid-Propellant Rocket Engines

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.229-231.1449 ◽

2012 ◽

Vol 229-231 ◽

pp. 1449-1453 ◽

Cited By ~ 1

Author(s):

Yan Jun Li ◽

Xiao Hui Peng ◽

Yu Qiang Cheng ◽

Jian Jun Wu

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Rocket Engine ◽

Fuzzy Model ◽

Rocket Engines ◽

Liquid Propellant ◽

Inference System ◽

Neuro Fuzzy ◽

Faulty Sensors ◽

Propellant Rocket

In this paper, the data of faulty sensors reconstruct algorithm of liquid-propellant rocket engine is developed based on adaptive neuro-fuzzy inference system. First, the input parameters selected for method is according to regularity criterion and the relationships between each parameter; second, adaptive neuro-fuzzy inference system is train by normal test, finally, the fuzzy mode is validated by normal data and the data of faulty sensor is reconstructed. The results indicate that this algorithm can reconstruct the data of faulty sensors accurately and show that the fuzzy model approach has good performance in faulty sensors data reconstruct for LRE.

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Estimate reliability of component-based software sys-tem using modified neuro fuzzy model

International Journal of Engineering & Technology ◽

10.14419/ijet.v6i2.7722 ◽

2017 ◽

Vol 6 (2) ◽

pp. 45 ◽

Cited By ~ 1

Author(s):

Ravi Kumar Sharma ◽

Dr. Parul Gandhi

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Fuzzy Model ◽

Software Systems ◽

Data Sets ◽

Component Reliability ◽

Inference System ◽

Neuro Fuzzy ◽

Proposed Model ◽

Estimate Reliability

There are many algorithms and techniques for estimating the reliability of Component Based Software Systems (CBSSs). Accurate esti-mation depends on two factors: component reliability and glue code reliability. Still much more research is expected to estimate reliability in a better way. A number of soft computing approaches for estimating CBSS reliability has been proposed. These techniques learnt from the past and capture existing patterns in data. In this paper, we proposed new model for estimating CBSS reliability known as Modified Neuro Fuzzy Inference System (MNFIS). This model is based on four factors Reusability, Operational, Component dependency, Fault Density. We analyze the proposed model for diffent data sets and also compare its performance with that of plain Fuzzy Inference System. Our experimental results show that, the proposed model gives better reliability as compare to FIS.

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Spike discharge prediction based on Neuro-fuzzy system

10.1101/133967 ◽

2017 ◽

Author(s):

Mahdi Zarei

Keyword(s):

Fuzzy Inference System ◽

Fuzzy System ◽

Fuzzy Inference ◽

Fuzzy Model ◽

Subtractive Clustering ◽

Inference System ◽

Spike Discharge ◽

Neuro Fuzzy ◽

Spike Latency ◽

Neural Fuzzy

AbstractThis paper presents the development and evaluation of different versions of Neuro-Fuzzy model for prediction of spike discharge patterns. We aim to predict the spike discharge variation using first spike latency and frequency-following interval. In order to study the spike discharge dynamics, we analyzed the Cerebral Cortex data of the cat from [29]. Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Wang and Mendel (WM), Dynamic evolving neural-fuzzy inference system (DENFIS), Hybrid neural Fuzzy Inference System (HyFIS), genetic for lateral tuning and rule selection of linguistic fuzzy system (GFS.LT.RS) and subtractive clustering and fuzzy c-means (SBC) algorithms are applied for data. Among these algorithms, ANFIS and GFS.LT.RS models have better performance. On the other hand, ANFIS and GFS.LT.RS algorithms can be used to predict the spike discharge dynamics as a function of first spike latency and frequency with a higher accuracy compared to other algorithms.

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Eccentricity Estimation in Ultra-Precision Rotating Devices Based on a Neuro-Fuzzy Model

10.20944/preprints201709.0104.v3 ◽

2017 ◽

Author(s):

Raúl Mario del Toro Matamoros ◽

Rodolfo Haber

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Fuzzy Model ◽

Rotation Frequency ◽

Vibration Signal ◽

Frequency Domain Analysis ◽

Performance Indices ◽

Inference System ◽

Neuro Fuzzy ◽

Ultra Precision

Monitoring complex electro-mechanical processes is not straightforward despite the arsenal of techniques nowadays availanle. This paper presents a method based on Adaptive-Network-based Fuzzy Inference System (ANFIS) to estimate eccentricity of its spinning axis. The method is experimentally tested on an ultra-precision rotating device commonly used for micro-scale turning. The developed model has three inputs, two obtained from a frequency domain analysis of a vibration signal and the third, which is the device rotation frequency. A comparative study demonstrates that an adaptive neural-fuzzy inference system model provides better error-based performance indices for detecting imbalance than a non-linear regression model. This simple, fast, and non-intrusive imbalance detection strategy is proposed to counteract eventual deterioration in the performance of ultra-high precision rotating machines due to vibrations.

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Penggunaan ANFIS pada Pengaturan Debit Air Berdasarkan Volume Air Dalam Tangki

ALINIER: Journal of Artificial Intelligence & Applications ◽

10.36040/alinier.v1i1.2519 ◽

2020 ◽

Vol 1 (1) ◽

pp. 24-32

Author(s):

Machrus Ali ◽

Ruslan Hidayat ◽

Iwan Cahyono

Keyword(s):

Neural Network ◽

Water Level ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Fuzzy Model ◽

Ball Valve ◽

Inference System ◽

Neuro Fuzzy ◽

Output Flow

Adaptive Neuro-Fuzzy Inference System (ANFIS) adalah penggabungan mekanisme Fuzzy Inference System (FIS) dan Neural Network (NN) yang digambarkan dalam arsitektur jaringan syaraf. Sistem inference fuzzy yang digunakan adalah sistem inference fuzzy model Tagaki-Sugeno-Kang (TSK) orde satu dengan pertimbangan kesederhanaan dan kemudahan komputasi. Pada penelitian ini sebagai pembanding didesain tanpa control, desain dengan PID standart, desain dengan Fuzzy Login Controller (FLC), dan ANFIS controller. Dalam desain penelitian ini yang dikontrol adalah ball valve electric pada tangki agar debit air yang keluar dari tangki sesuai dengan yang dibutuhkan dalam proses produksi dengan menggunakan empat control. Dari simulasi diapatkan bahwa Dsain Water Level yang paling baik pada percobaan ini adalah menggunakan metode ANFIS dengan nilai overshot dan undershot terkecil pada water level dan output flow. Sehingga desain ini bias dipakai acuan untuk menghasilkan control aliran air sesuai dengan harapan yang diinginkan. Hasil simulasi ini akan dibandingkan lagi dengan metode kecerdasan buatan yang lain, sehingga adan didapatkan hasil yang paling sesuai.

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The Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Solar Energy and Sustainable Development ◽

10.51646/jsesd.v5i2.83 ◽

2021 ◽

Vol 5 (2) ◽

Author(s):

Muna A Alzukrah ◽

Yosof M Khalifa

Keyword(s):

Solar Radiation ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Fuzzy Model ◽

Global Solar Radiation ◽

Percentage Error ◽

Data Set ◽

Inference System ◽

Energy Applications ◽

Neuro Fuzzy

The prediction of solar radiation is very important tool in climatology, hydrology and energy applications, as it permits estimating solar data for locations where measurements are not available. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is presented to predict the monthly global solar radiation on a horizontal surface in Libya. The real meteorological solar radiation data from 5 stations for the period of 1982 - 2009 with diffrent latitudes and longitudes were used in the current study. The data set is divided into two subsets; the fist is used for training and the latter is used for testing the model. (ANFIS) combines fuzzy logic and neural network techniques that are used in order to gain more effiency. The statistical performance parameters such as root mean square error (RMSE), mean absolute percentage error (MAPE) and the coeffient of effiency (E) were calculated to check the adequacy of the model. On the basis of coeffient of effiency, as well as the scatter diagrams and the error modes, the predicted results indicate that the neuro-fuzzy model gives reasonable results: accuracy of about 92% - 96% and the RMSE ranges between 0.22 - 0.35 kW.hr/m2/day

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Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model

Hydrology Research ◽

10.2166/nh.2017.163 ◽

2017 ◽

Vol 49 (1) ◽

pp. 27-40 ◽

Cited By ~ 11

Author(s):

Honey Badrzadeh ◽

Ranjan Sarukkalige ◽

A. W. Jayawardena

Keyword(s):

Time Series ◽

Fuzzy Inference System ◽

Stream Flow ◽

Fuzzy Inference ◽

Fuzzy Model ◽

Performance Criteria ◽

Discrete Wavelet ◽

Inference System ◽

Input Selection ◽

Neuro Fuzzy

Abstract In this paper, an advanced stream flow forecasting model is developed by applying data-preprocessing techniques on adaptive neuro-fuzzy inference system (ANFIS). Wavelet multi-resolution analysis is coupled with an ANFIS model to develop a hybrid wavelet neuro-fuzzy (WNF) model. Different models with different input selection and structures are developed for daily, weekly and monthly stream flow forecasting in Railway Parade station on Ellen Brook River, Western Australia. The stream flow time series is decomposed into multi-frequency time series by discrete wavelet transform using the Haar, Coiflet and Daubechies mother wavelets. The wavelet coefficients are then imposed as input data to the neuro-fuzzy model. Models are developed based on Takagi-Sugeno-Kang fuzzy inference system with the grid partitioning approach for initializing the fuzzy rule-based structure. Mean-square error and Nash-Sutcliffe coefficient are chosen as the performance criteria. The results of the application show that the right selection of the inputs with high autocorrelation function improves the accuracy of forecasting. Comparing the performance of the hybrid WNF models with those of the original ANFIS models indicates that the hybrid WNF models produce significantly better results especially in longer-term forecasting.

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