scholarly journals Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

2021 ◽  
Vol 11 (14) ◽  
pp. 6590
Author(s):  
Krittakom Srijiranon ◽  
Narissara Eiamkanitchat
Keyword(s):  
Particulate Matter ◽  
Fuzzy Model ◽  
Original Data ◽  
Northern Region ◽  
National Standard ◽  
Minimum Entropy ◽  
Fuzzy Transformation ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Entropy Principle

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

A New Knowledge Discovery Model for Extracting Diagnosis Rules of Manufacturing Process

2006 ◽  
Vol 505-507 ◽  
pp. 889-894
Author(s):  
Ying Chieh Tsai ◽  
Ching Hsue Cheng ◽  
Jing Rong Chang
Keyword(s):  
Knowledge Discovery ◽  
Manufacturing Process ◽  
Conveyor Belt ◽  
Membership Functions ◽  
Minimum Entropy ◽  
Manufacturing Line ◽  
Proposed Model ◽  
New Knowledge ◽  
Entropy Principle ◽  
Correlation Based Feature Selection

The knowledge obtained from the experience of monitoring manufacturing process is critical to guarantee good products produced at the end of manufacturing line. Recently, many methods have been developed for the described purpose above. In this paper, a new knowledge discovery model based on soft computing is proposed. The proposed model contains a new algorithm Modified Correlation-based Feature Selection (MCFS), a new algorithm Modified Minimum Entropy Principle Algorithm (MMEPA), and Variable Precision Rough Set Model (VP-model). After conducting a real case of monitoring the process of manufacturing industrial conveyor belt, some advantages of the proposed model are that (1) MCFS can quickly identifying and screening irrelevant, redundant, and noisy features for data reduction; (2) MMEPA can objectively construct membership functions of fuzzy sets for fuzzifing the reduced dataset; (3) VP-model can extract causal relationship rules for controlling product quality; (4) Extracted rules by the proposed knowledge discovery model are easily understood and interpretable.

scholarly journals A NEURO FUZZY MODEL FOR THE INVESTIGATION OF DETERIORATION OF METALLIC PIPE CONVEYING FLUID UNDER DIFFERENT PIPE BURIAL DEPTH, SOIL TYPES AND PROPERTIES

2016 ◽  
Vol 36 (1) ◽  
pp. 72-79
Author(s):  
TT Akano ◽  
OA Fakindele ◽  
HE Mgbemere ◽  
JC Amechi
Keyword(s):  
Soil Properties ◽  
Membership Function ◽  
Fuzzy Model ◽  
Soil Types ◽  
Burial Depth ◽  
Pipe Conveying Fluid ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Conveying Fluid

Several factors may contribute directly or indirectly to the structural failure of metallic pipes. The most important of which is corrosion. Corrosivity of pipes is not a directly measurable parameter as pipe corrosion is a very random phenomenon. The main aim of the present study is to develop a neuro-fuzzy model capable of establishing corrosion rate criterion as a function of pipe burial depth, soil types, and properties for the prediction of deterioration of metallic pipe conveying fluid. The proposed model includes a fuzzy model and the artificial neural network (ANN) to determine soil corrosivity potential (CoP) based on soil properties. The combination contains the data of linguistic variables characterising various soil properties, and learning capability of the system that constructs relationships among those soil properties and CoP. Subsequently, the artificial neuro-fuzzy inference system (ANFIS) maps each element of its input membership function to an output membership function between 0 and 1 to determine the deterioration rate (CoP) of metallic fluid-conveying-pipe. Field data from buried fluid pipes were examined to illustrate the application of the proposed model. The ultimate goal is the ability to access the current and future life of oil pipe, given a set of circumstances, and also appropriate adoptable methodology in view of a preventive maintenance measure for the pipes in a given operating environment. Results reveal that with more than 40% clay content quickens corrosion of buried fluid pipes more than any other considered factor. http://dx.doi.org/10.4314/njt.v36i1.10

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

2017 ◽  
Vol 6 (2) ◽  
pp. 45 ◽  
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.

New fuzzy solution for determining anticipation and evaluation behavior during car-following maneuvers

2017 ◽  
Vol 232 (7) ◽  
pp. 936-945 ◽  
Author(s):  
Ali Ghaffari ◽  
Alireza Khodayari ◽  
Ali Kamali ◽  
Farzam Tajdari ◽  
Niloofar Hosseinkhani
Keyword(s):  
Evaluation Model ◽  
Fuzzy Model ◽  
Intelligent Vehicles ◽  
Lane Change ◽  
Car Following ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Driving Assistance ◽  
Important Means ◽  
Two Stages

Nowadays, vehicles are the most important means of transportation in our daily lifes. During the last few decades, many studies have been carried out in the field of intelligent vehicles and significant results on the behavior of car-following and lane-change maneuvers have been achieved. However, the effects of lane-change on the car-following models have been relatively neglected. This effect is a temporary state in car-following behavior during which the follower vehicle considerably deviates from conventional car-following models for a limited time. This paper aims to investigate the behavior of the immediate follower during the lane-change of its leader vehicle. Based on a closer inspection of the microstructure behavior of real drivers, this temporary state is divided into two stages of anticipation and evaluation. Afterwards, a novel and adaptive neuro-fuzzy model that considers human driving factors is proposed to simulate the behavior of real drivers. Comparison between model results and real traffic data reveals that the proposed model can describe anticipation and evaluation behavior with smaller errors. The anticipation and evaluation model can modify current car-following models so as to accurately simulate the behavior of an immediate follower which leads to an enhancement of car-following applications such as driving assistance and collision avoidance systems.

DEVELOPMENT OF A NEURO FUZZY MODEL FOR NOISE PREDICTION IN OPENCAST MINES

2009 ◽  
Vol 17 (05) ◽  
pp. 729-745 ◽  
Author(s):  
SANTOSH KUMAR NANDA ◽  
DEBI PRASAD TRIPATHY ◽  
SARAT KUMAR PATRA
Keyword(s):  
Sound Pressure ◽  
Prediction Models ◽  
Fuzzy Model ◽  
Complex Model ◽  
Noise Prediction ◽  
Opencast Mining ◽  
Noise Levels ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Opencast Mines

The need of developing appropriate noise prediction models for finding out the accurate status of noise levels (>90 dBA) generated from various opencast mining machineries is overdue. The measured sound pressure levels (SPL) of equipments are not accurate due to instrumental error, attenuation due to geometrical aberration, atmospheric attenuation etc. Some of the popular noise prediction models e.g. VDI and ENM have been applied in mining and allied industries. Among these models, VDI2714 is simple and less complex model. In this paper, a neuro-fuzzy model is proposed to predict the machinery noise in an opencast coal mine. The proposed model is trained with VDI2714 and the model output is seen very closely to matching with VDI2714 output. The model proposed has a mean square error of 2.73%. This model takes CPU time of nearly 0.0625 sec where as it takes 0.5 sec for VDI2714 i.e. approximately twelve times faster.

A NEURO-FUZZY MODEL FOR DIMENSIONALITY REDUCTION AND ITS APPLICATION

2007 ◽  
Vol 15 (05) ◽  
pp. 571-593 ◽  
Author(s):  
VITALIY KOLODYAZHNIY ◽  
FRANK KLAWONN ◽  
KATHARINA TSCHUMITSCHEW
Keyword(s):  
Dimensionality Reduction ◽  
Treatment Plant ◽  
Fuzzy Model ◽  
Principal Component ◽  
Nonlinear Dimensionality Reduction ◽  
Training Algorithms ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Plant Data ◽  
Hidden Layer

A novel neuro-fuzzy approach to nonlinear dimensionality reduction is proposed. The approach is an auto-associative modification of the Neuro-Fuzzy Kolmogorov's Network (NFKN) with a “bottleneck” hidden layer. Two training algorithms are considered. The validity of theoretical results and the advantages of the proposed model are confirmed by an experiment in nonlinear principal component analysis and an application in the visualization of high-dimensional wastewater treatment plant data.

NEURO-FUZZY MODEL FOR ENVIRONMENTAL IMPACT ASSESSMENT

2011 ◽  
Vol 10 (3) ◽  
pp. 381-386 ◽  
Author(s):  
Alexandru Trandabat ◽  
Marius Pislaru ◽  
Silvia Avasilcai
Keyword(s):  
Environmental Impact ◽  
Impact Assessment ◽  
Environmental Impact Assessment ◽  
Fuzzy Model ◽  
Neuro Fuzzy

Characterization of polymer based composite using neuro-fuzzy model

2021 ◽  
Author(s):  
Safaa A.S. Almtori ◽  
Imad O. Bachi Al-Fahad ◽  
Atheed Habeeb Taha Al-temimi ◽  
A.K. Jassim
Keyword(s):  
Fuzzy Model ◽  
Neuro Fuzzy

Determining the safety culture in a gun factory in Turkey: A fuzzy approach

2020 ◽  
pp. 1-11
Author(s):  
Gökçen A. Çiftçioğlu ◽  
Mehmet A. N. Kadırgan ◽  
Ahmet Eşiyok
Keyword(s):  
Safety Culture ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Self Awareness ◽  
Inference System ◽  
Five Factors ◽  
Initial Questionnaire ◽  
Proposed Model ◽  
Assessment Questionnaire

Safety culture is a very complex phenomenon due to its intangible nature. It is tough to measure and express it with numerical values, as there is no simple indicator to measure it. This paper presents a fuzzy inference system that measures the safety culture. First of all, a safety culture assessment questionnaire is developed by utilizing related literature. The initial questionnaire had 29 items. The questionnaire is applied to 259 employees within the gun manufacturing factory. After making an exploratory factor analysis, the questionnaire is based on five factors with 25 items. The safety culture indicators are defined as; safety follow-up audit reporting, employees’ self-awareness, operational safety commitment, management’s safety commitment, safety orientedness. Normality, reliability, and correlation analysis are performed. Then a fuzzy model is constructed with five inputs and one output. The inputs are the five factors mentioned above, and the output generated is the safety culture result, which is between 0-1. The presented fuzzy model produces reliable results indicating the safety culture level from the employees’ eyes. Beyond exploring the employees’ safety culture, the proposed model can easily be understood by the practitioners from various sectors. Furthermore, the model is straightforward to customize for various fields of industry.

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