A combination of genetic algorithm-based fuzzy C-means with a convex hull-based regression for real-time fuzzy switching regression analysis: application to industrial intelligent data analysis

2013 ◽  
Vol 9 (1) ◽  
pp. 71-82 ◽  
Author(s):  
Azizul Azhar Ramli ◽  
Junzo Watada ◽  
Witold Pedrycz
Keyword(s):  
Genetic Algorithm ◽  
Regression Analysis ◽  
Data Analysis ◽  
Convex Hull ◽  
Real Time ◽  
Intelligent Data Analysis ◽  
Switching Regression ◽  
Fuzzy C Means ◽  
Analysis Application

An Efficient Solution of Real-Time Fuzzy Regression Analysis to Information Granules Problem

2012 ◽  
Vol 16 (2) ◽  
pp. 199-209 ◽  
Author(s):  
Azizul Azhar Ramli ◽  
◽  
Junzo Watada ◽  
Witold Pedrycz ◽  
◽  
...  
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
Convex Hull ◽  
Real Time ◽  
Clustering Algorithm ◽  
System Modeling ◽  
Computing Time ◽  
Fuzzy Regression ◽  
Information Granules ◽  
Hull Structure

Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-based Fuzzy C-Means (GAFCM) and a convex hull-based regression approach being regarded as a potential solution to the formation of information granules. It is shown that a setting of Granular Computing helps us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time information granules regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design sub-convex hulls as well as a main convex hull structure. In the proposed design setting, we emphasize a pivotal role of the convex hull approach or more specifically the Beneath-Beyond algorithm, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.

Kernel fuzzy C- means clustering with teaching learning based optimization algorithm (TLBO-KFCM)

2021 ◽  
pp. 1-9
Author(s):  
Saumya Singh ◽  
Smriti Srivastava
Keyword(s):  
Genetic Algorithm ◽  
Data Analysis ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Data Sets ◽  
New Approach ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM).

scholarly journals IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0

2018 ◽  
Vol 101 ◽  
pp. 138-146 ◽  
Author(s):  
Ricardo Silva Peres ◽  
Andre Dionisio Rocha ◽  
Paulo Leitao ◽  
Jose Barata
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Industry 4.0 ◽  
Intelligent Data Analysis

Real-time fuzzy regression analysis: A convex hull approach

2011 ◽  
Vol 210 (3) ◽  
pp. 606-617 ◽  
Author(s):  
Azizul Azhar Ramli ◽  
Junzo Watada ◽  
Witold Pedrycz
Keyword(s):  
Regression Analysis ◽  
Convex Hull ◽  
Real Time ◽  
Fuzzy Regression ◽  
Fuzzy Regression Analysis

DTI data analysis: application of fiber tracking to group averaged data sets

2010 ◽  
Vol 41 (01) ◽  
Author(s):  
HP Müller ◽  
A Unrath ◽  
A Riecker ◽  
AC Ludolph ◽  
J Kassubek
Keyword(s):  
Data Analysis ◽  
Fiber Tracking ◽  
Data Sets ◽  
Analysis Application

Pengaruh Motivasi, Self Awareness Dan Komunikasi terhadap Disiplin Kerja Karyawan di Perusahaan Daerah Air Minum (Pdam) Kabupaten Ponorogo

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Fitriana Fitriana ◽  
Umi Farida ◽  
Tegoeh Hari Abrianto
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
Water Supply ◽  
Regression Coefficient ◽  
Reliability Test ◽  
Self Awareness ◽  
Validity And Reliability ◽  
Communication Variable ◽  
Water Supply Company ◽  
Regional Water

This study aims to determine the effect of motivation, self awareness and communication on the work discipline of employees in the Regional Water Supply Company (PDAM) of Ponorogo Regency. The location of research in Pramuka Street Number 21, Nologaten, Ponorogo Regency. The population in this study was 102 employees. The sample in this study used 50 respondents. Data collection techniques using questionnaires, then tested with validity and reliability test, while the method of data analysis using multiple regression analysis with the help of SPSS and hypothesis testing partially or simultaneously. The results showed that; (1) Motivation partially influences the work discipline of employees in the Regional Water Supply Company (PDAM) of Ponorogo Regency with a regression coefficient of 0.317, t value of 2.903> t table of 2.012 and sig. of 0.006 <0.05, (2) Self Awareness partially influences the work discipline of employees in the Regional Water Supply Company (PDAM) of Ponorogo Regency with the results of the regression coefficient of 0.409, t value of 3.478> t table of 2.012 and sig. of 0.001 <0.05, (3) Communication partially influences the work discipline of employees in the Regional Water Supply Company (PDAM) of Ponorogo Regency with the results of a regression coefficient of 0.310, t value of 2.178> t table of 2.012 and sig. of 0.035 <0.05, (4) Motivation, self awareness and communication simultaneously affect employee work discipline in the Regional Water Supply Company (PDAM) of Ponorogo Regency with the calculated F value of 14.807> F table 2.81 and sig value. of 0,000 <0.05, (5) Self awareness is the most dominant variable affecting the work discipline of employees in the Regional Water Supply Company (PDAM) of Ponorogo Regency with the result of self awareness variable t value of 3.478 is greater than the value of t variable count motivation and communication variables. Furthermore, from the value of sig. the variable self awareness of 0.001 is smaller than the value of sig. motivation variable and communication variable.

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