The importance measure of fuzzy input on failure credibility under the fuzzy uncertainty

Zhang, Xiaobo; Lü, Zhenzhou; Wang, Lu; Li, Guijie

doi:10.1016/j.ast.2020.106320

Cited by 9 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the quality of the proposed method is estimated using the V-fold cross-validation technique [9] .…”

Section: V-fold Cross Validationmentioning

confidence: 99%

“…As an extension to interval methods, fuzzy number approaches assign a membership function to all parameter values which are related to a certain interval [8] . As the related membership functions are defined more easily, in practical applications, there is a lower but more flexible data requirement in fuzzy mathematical programming (FMP) [9] . Recently, due to its excellent performance, fuzzy logic has a wide application in uncertainty quantification in engineering [10,11] .…”

mentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Fuzzy Inference System Model to Analyze Fuzzy Regression with Quadratic Programming and Fuzzy Weights Incorporating Uncertainty in the Observed Data

Danesh¹,

Danesh²,

Maleki³

et al. 2023

Fuzzy Information and Engineering

View full text Add to dashboard Cite

To address the compounded uncertainty in the observed output data, we introduce a new method of fuzzy regression modeling which is based on quadratic programming and fuzzy weights, so that the objective function represents the quadratic error for all of the central tendencies and the spreads. Also, the fuzzy weights are optimized for the fuzzy regression model estimation with crisp input and fuzzy output based on the adaptive fuzzy networks, considering symmetrical triangular fuzzy output. This paper aims to use the proposed method for the prediction of the output value in empirical applications where the observed value is a range or mean of several values, rather than a real fixed number. Two numerical examples were employed to demonstrate the efficiency of the method and compare the results of the proposed method with the previous ones such as linear programming (LP), quadratic programming (QP), as well as combination of linear programming and fuzzy weights (FWLP). The results show that the proposed method provides better prediction accuracy than other methods in surface roughness prediction of the grinding process.

show abstract

“…In this paper, the quality of the proposed method is estimated using the V-fold cross-validation technique [9] .…”

Section: V-fold Cross Validationmentioning

confidence: 99%

mentioning

confidence: 99%

An Adaptive Fuzzy Inference System Model to Analyze Fuzzy Regression with Quadratic Programming and Fuzzy Weights Incorporating Uncertainty in the Observed Data

Danesh¹,

Danesh²,

Maleki³

et al. 2023

Fuzzy Information and Engineering

View full text Add to dashboard Cite

show abstract

“…Recently, the application of fuzzy logic has widely increased in uncertainty quantification for its successful performance. , Zhang 2020) . The linear model of fuzzy regression analysis, established by , *corresponding author has enabled the fuzzy system to give the fuzzy output.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of adaptive neuro-fuzzy inference system using PSO and ant colony optimization for estimation of uncertain observed values

Danesh,

Danesh

2023

Soft Comput

View full text Add to dashboard Cite

This study employs a new method for regression model prediction in an uncertain environment and presents fuzzy parameter estimation of fuzzy regression models using triangular fuzzy numbers. These estimation methods are obtained by new learning algorithms in which linear programming is used. In this study, the new algorithm is a combination of a fuzzy rule-based system, on the basis of particle swarm optimization (PSO) and ant Colony Optimization AC𝑂 𝑅 . In addition, a simulation and a practical example in the field of machining process are applied to indicate the performance of the proposed methods in dealing with problems where the observed variables have the nature of uncertainty and randomness. Finally, the results of the proposed algorithms are evaluated.

show abstract

A new chance reliability-based design optimization approach considering aleatory and epistemic uncertainties

Zhou

Zhang²,

Zhang³

et al. 2022

Struct Multidisc Optim

View full text Add to dashboard Cite

The importance measure of fuzzy input on failure credibility under the fuzzy uncertainty

Cited by 9 publications

References 43 publications

An Adaptive Fuzzy Inference System Model to Analyze Fuzzy Regression with Quadratic Programming and Fuzzy Weights Incorporating Uncertainty in the Observed Data

An Adaptive Fuzzy Inference System Model to Analyze Fuzzy Regression with Quadratic Programming and Fuzzy Weights Incorporating Uncertainty in the Observed Data

Optimal design of adaptive neuro-fuzzy inference system using PSO and ant colony optimization for estimation of uncertain observed values

A new chance reliability-based design optimization approach considering aleatory and epistemic uncertainties

Contact Info

Product

Resources

About