A probabilistic and statistical view of fuzzy methods
Technometrics
Entropy-based fuzzy clustering and fuzzy modeling
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue: Preference modelling 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
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
A cluster validity index for fuzzy clustering
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
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
Information Sciences: an International Journal
Fuzzy logic based assignable cause diagnosis using control chart patterns
Information Sciences: an International Journal
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
A clustering algorithm for multiple data streams based on spectral component similarity
Information Sciences: an International Journal
Hi-index | 0.07 |
Control charts are the most popular Statistical Process Control (SPC) tools used to monitor process changes. When a control chart produces an out-of-control signal, it means that the process has changed. However, control chart signals do not indicate the real time of the process changes, which is essential for identifying and removing assignable causes and ultimately improving the process. Identifying the real time of the process change is known as change-point estimation problem. Most of the traditional change-point methods are based on maximum likelihood estimators (MLE) which need strict statistical assumptions. In this paper, first, we introduce clustering as a potential tool for change-point estimation. Next, we discuss the challenges of employing clustering methods for change-point estimation. Afterwards, based on the concepts of fuzzy clustering and statistical methods, we develop a novel hybrid approach which is able to effectively estimate change-points in processes with either fixed or variable sample size. Using extensive simulation studies, we also show that the proposed approach performs considerably well in all considered conditions in comparison to powerful statistical methods and popular fuzzy clustering techniques. The proposed approach can be employed for processes with either normal or non-normal distributions. It is also applicable to both phase-I and phase-II. Finally, it can estimate the true values of both in- and out-of-control states' parameters.