A probabilistic and statistical view of fuzzy methods
Technometrics
Fuzzy Sets and Systems - Special issue: Preference modelling and applications
&agr;-Cut fuzzy control charts for linguistic data
International Journal of Intelligent Systems
Expert Systems with Applications: An International Journal
An alternative approach to fuzzy control charts: Direct fuzzy approach
Information Sciences: an International Journal
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
Expert Systems with Applications: An International Journal
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Optimization design of control charts based on minimax decision criterion and fuzzy process shifts
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Development of fuzzy process control charts and fuzzy unnatural pattern analyses
Computational Statistics & Data Analysis
Fuzzy process control: construction of control charts with fuzzy numbers
Fuzzy Sets and Systems
The efficacy of fuzzy representations of uncertainty
IEEE Transactions on Fuzzy Systems
Data clustering by minimizing disconnectivity
Information Sciences: an International Journal
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
On the robustness of Type-1 and Interval Type-2 fuzzy logic systems in modeling
Information Sciences: an International Journal
Computers and Industrial Engineering
Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling
Information Sciences: an International Journal
Hi-index | 0.07 |
Despite their capability in monitoring the variability of the processes, control charts are not effective tools for identifying the real time of such changes. Identifying the real time of the change in a process is recognized as change-point estimation problem. Most of the change-point models in the literature are limited to fixed sampling control charts which are only a special case of more effective charts known as variable sampling charts. In this paper, we develop a general fuzzy-statistical clustering approach for estimating change-points in different types of control charts with either fixed or variable sampling strategy. For this purpose, we devise and evaluate a new similarity measure based on the definition of operation characteristics and power functions. We also develop and examine a new objective function and discuss its relation with maximum-likelihood estimator. Finally, we conduct extensive simulation studies to evaluate the performance of the proposed approach for different types of control charts with different sampling strategies.