scholarly journals Hierarchical clusterization fuzzy data in tensor basis

2011 ◽  
Vol 3 (35) ◽  
Author(s):  
Ю. Н. Минаев ◽  
О. Ю. Филимонова ◽  
Ю. И. Минаева
Keyword(s):  
Fuzzy Data ◽  
Tensor Basis

Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function

2018 ◽  
pp. 397
Author(s):  
Nadia Hashim Al-Noor ◽  
Shurooq A.K. Al-Sultany
Keyword(s):  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Mean Squared Error ◽  
Maximum Likelihood Estimators ◽  
Reliability Function ◽  
Fuzzy Data ◽  
Monte Carlo Simulation Study ◽  
Weibull Parameters ◽  
Squared Error ◽  
Newton Raphson

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.

XML Modeling of Fuzzy Data with Relational Databases

2011 ◽  
Vol 34 (2) ◽  
pp. 291-303 ◽  
Author(s):  
Li YAN ◽  
Zong-Min MA ◽  
Jian LIU ◽  
Fu ZHANG
Keyword(s):  
Relational Databases ◽  
Fuzzy Data

scholarly journals Tensor-structured algorithm for reduced-order scaling large-scale Kohn–Sham density functional theory calculations

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Chih-Chuen Lin ◽  
Phani Motamarri ◽  
Vikram Gavini
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Large Scale ◽  
Density Functional Theory Calculations ◽  
System Size ◽  
Real Space ◽  
Functional Theory ◽  
Reduced Order ◽  
Separable Approximation ◽  
Tensor Basis

AbstractWe present a tensor-structured algorithm for efficient large-scale density functional theory (DFT) calculations by constructing a Tucker tensor basis that is adapted to the Kohn–Sham Hamiltonian and localized in real-space. The proposed approach uses an additive separable approximation to the Kohn–Sham Hamiltonian and an L1 localization technique to generate the 1-D localized functions that constitute the Tucker tensor basis. Numerical results show that the resulting Tucker tensor basis exhibits exponential convergence in the ground-state energy with increasing Tucker rank. Further, the proposed tensor-structured algorithm demonstrated sub-quadratic scaling with system-size for both systems with and without a gap, and involving many thousands of atoms. This reduced-order scaling has also resulted in the proposed approach outperforming plane-wave DFT implementation for systems beyond 2000 electrons.

scholarly journals Outlier Detection Algorithms Over Fuzzy Data with Weighted Least Squares

2021 ◽  
Author(s):  
Natalia Nikolova ◽  
Rosa M. Rodríguez ◽  
Mark Symes ◽  
Daniela Toneva ◽  
Krasimir Kolev ◽  
...  
Keyword(s):  
Least Squares ◽  
Outlier Detection ◽  
Weighted Least Squares ◽  
Fuzzy Data ◽  
Detection Algorithms

scholarly journals Complex Uncertainty of Surface Data Modeling via the Type-2 Fuzzy B-Spline Model

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1054
Author(s):  
Rozaimi Zakaria ◽  
Abd. Fatah Wahab ◽  
Isfarita Ismail ◽  
Mohammad Izat Emir Zulkifly
Keyword(s):  
Complex Surface ◽  
Complex Data ◽  
Fuzzy Data ◽  
Control Points ◽  
B Spline ◽  
Spline Surface ◽  
Data Control ◽  
Surface Data ◽  
Model Complex

This paper discusses the construction of a type-2 fuzzy B-spline model to model complex uncertainty of surface data. To construct this model, the type-2 fuzzy set theory, which includes type-2 fuzzy number concepts and type-2 fuzzy relation, is used to define the complex uncertainty of surface data in type-2 fuzzy data/control points. These type-2 fuzzy data/control points are blended with the B-spline surface function to produce the proposed model, which can be visualized and analyzed further. Various processes, namely fuzzification, type-reduction and defuzzification are defined to achieve a crisp, type-2 fuzzy B-spline surface, representing uncertainty complex surface data. This paper ends with a numerical example of terrain modeling, which shows the effectiveness of handling the uncertainty complex data.

scholarly journals Fuzzy data analysis and classification

2017 ◽  
Vol 11 (4) ◽  
pp. 645-657 ◽  
Author(s):  
Pierpaolo D’Urso ◽  
María Ángeles Gil
Keyword(s):  
Data Analysis ◽  
Fuzzy Data

scholarly journals Innovative Correlation Coefficient Measurement with Fuzzy Data

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Berlin Wu ◽  
Chin Feng Hung
Keyword(s):  
Mathematics Achievement ◽  
Correlation Coefficient ◽  
Empirical Studies ◽  
Correlation Coefficients ◽  
Interval Data ◽  
Fuzzy Data ◽  
Pearson’S Correlation Coefficient ◽  
Coefficient Measurement ◽  
The Relationship ◽  
Fuzzy Interval

Correlation coefficients are commonly found with crisp data. In this paper, we use Pearson’s correlation coefficient and propose a method for evaluating correlation coefficients for fuzzy interval data. Our empirical studies involve the relationship between mathematics achievement and other projects.

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