Journal Article10.1016/J.INS.2009.01.019
A hybrid fuzzy-statistical clustering approach for estimating the time of changes in fixed and variable sampling control charts
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TL;DR: A novel hybrid approach is developed which is able to effectively estimate change-points in processes with either fixed or variable sample size and can estimate the true values of both in- and out-of-control states' parameters.
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About: This article is published in Information Sciences. The article was published on 01 May 2009. The article focuses on the topics: Fuzzy clustering & Statistical process control.
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Citations
Fuzzy logic based assignable cause diagnosis using control chart patterns
TL;DR: A rule based fuzzy inference system is developed for [email protected]?
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Change Point Estimation of Multivariate Linear Profiles Under Linear Drift
TL;DR: Performance of the proposed maximum likelihood estimators is compared under linear drift changes in the regression parameters when a combined MEWMA and Chi-square control charts method signals an out-of-control condition.
25
Change Point Estimation in the Mean of Multivariate Linear Profiles with No Change Type Assumption via Dynamic Linear Model
TL;DR: The maximum likelihood approach is developed to estimate change point in the mean of multivariate linear profiles in Phase II and effect of different values of the Multivariate Exponentially Weighted Moving Average control chart smoothing coefficient on the performance of the proposed estimator is investigated, presenting that the smoothing estimator has more uniform performance.
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A hybrid method for estimating the process change point using support vector machine and fuzzy statistical clustering
M.S. Kazemi,K. Kazemi,M. A. Yaghoobi,H. Bazargan +3 more
- 01 Mar 2016
TL;DR: A hybrid method for estimating the change point on x ?
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Sustainable risk management: fuzzy approach to volatility and application on FTSE 100 index
TL;DR: A fuzzy volatility labeling algorithm is offered to detect the periods with abnormal activities on daily share returns and is believed that this algorithm may be helpful to construct different estimation models for the time periods with normal and abnormal activities.
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