Information Sciences: an International Journal
A fuzzy statistical test of fuzzy hypotheses
Fuzzy Sets and Systems
Statistical tests for fuzzy data
Fuzzy Sets and Systems
Testing statistical hypotheses with vague data
Fuzzy Sets and Systems
Information Sciences—Informatics and Computer Science: An International Journal
Uncertain probabilities II: the continuous case
Soft Computing - A Fusion of Foundations, Methodologies and Applications
&agr;-Cut fuzzy control charts for linguistic data
International Journal of Intelligent Systems
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
An alternative approach to fuzzy control charts: Direct fuzzy approach
Information Sciences: an International Journal
λ-Statistical limit points of the sequences of fuzzy numbers
Information Sciences: an International Journal
Solution of non-linear fuzzy systems by decomposition of incremental fuzzy numbers
Information Sciences: an International Journal
Upper and lower values for the level of fuzziness in FCM
Information Sciences: an International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
A hybrid fuzzy adaptive sampling - Run rules for Shewhart control charts
Information Sciences: an International Journal
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Development of fuzzy process control charts and fuzzy unnatural pattern analyses
Computational Statistics & Data Analysis
On the use of words and fuzzy sets
Information Sciences: an International Journal
Fuzzy logic based assignable cause diagnosis using control chart patterns
Information Sciences: an International Journal
The n-dimensional fuzzy sets and Zadeh fuzzy sets based on the finite valued fuzzy sets
Computers & Mathematics with Applications
Computational intelligence approach to PID controller design using the universal model
Information Sciences: an International Journal
Information Sciences: an International Journal
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Statistical process control (SPC) is an approach that uses statistical techniques to monitor the process. Shewhart introduced the control charts that are one of the most important techniques of quality control to detect if assignable causes exist. The widely used control charts are and . These are called traditional variable control charts. In the traditional variable control charts, center line, upper control limit and lower control limit are represented by numeric values. A process is either "in control" or "out of control" depending on numeric observation values. For many problems, control limits could not be so precise. Uncertainty comes from the measurement system including operators and gauges, and environmental conditions. In this context, fuzzy set theory is a useful tool to handle this uncertainty. Numeric control limits can be transformed to fuzzy control limits by using membership functions. If a sample mean is too close to the control limits and the used measurement system is not so sensitive, the decision may be faulty. Fuzzy control limits provide a more accurate and flexible evaluation. This study constructs the fuzzy and control charts with @a-cuts. An application is presented for fuzzy control charts. By using fuzzy and control charts, the flexibility of traditional control limits is increased.