Citation map · Study

SZodixStudyAcademic Library

Shelves Open access

Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated

Recognition of unnatural patterns in process control charts through combining two types of neural networks

14 references·8 citing·27 second hop

FromToDepth

this work references cited by second hop

References

1992Introduction to Statistical Quality Control7,842 cited
1990Statistical Quality Control885 cited
2001Introduction to Statistical Quality Control770 cited
1997A neural network approach for the analysis of control chart patterns169 cited
1995A multi-layer neural network model for detecting changes in the process mean145 cited
1994Control chart pattern recognition using learning vector quantization networks140 cited
1997Statistical Quality Control134 cited
1999A neural network based model for abnormal pattern recognition of control charts115 cited
1999Recognition of control chart concurrent patterns using a neural network approach111 cited
2005A hybrid learning-based model for on-line detection and analysis of control chart patterns103 cited
1996A neural fuzzy control chart for detecting and classifying process mean shifts90 cited
1999On-line control chart pattern detection and discrimination—a neural network approach81 cited
2001Shifts recognition in correlated process data using a neural network41 cited
2004OPTIMIZING FEEDFORWARD NEURAL NETWORKS FOR CONTROL CHART PATTERN RECOGNITION THROUGH GENETIC ALGORITHMS31 cited

Cited by

2022

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

Concurrent Control Chart Pattern Recognition: A Systematic Review

26 cited

2019Control Chart Pattern Recognition Method Based on Improved One-dimensional Convolutional Neural Network23 cited

2015A hybrid method for estimating the process change point using support vector machine and fuzzy statistical clustering22 cited

2013Recognition and classification of single and concurrent unnatural patterns in control charts via neural networks and fitted line of samples19 cited

2020Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process18 cited

2021Adaptive Shewhart Control Charts Under Fuzzy Parameters with Tuned Particle Swarm Optimization Algorithm13 cited

2012A double neural network approach for the identification and parameter estimation of control chart patterns11 cited

2018Development of fitted line and fitted cosine curve for recognition and analysis of unnatural patterns in process control charts9 cited