Multilayer perceptions for detecting cyclic data on control charts

REFERENCES

• BENEKE , M. , LEEMIS , L. M. , SCHEGEL , R. E. , and FOOTE , B. L. , 1988 , Spectral analysis in quality control a control chart based on the periodiogram . Technometrics , 30 ( 1 ), 63 – 70 .
   Web of Science ®Google Scholar
• BLOOMFIELD , P. , 1976 , Fourier Analysis of Time Series An Introduction ( New York Wiley ).
   Google Scholar
• CHANG , S. I. , and Aw , C. A. , 1994 , A neural network control chart for detecting process mean shifts . Proceedings of the Third Industrial Engineering Research Conference , pp. 337 – 342 .
   Google Scholar
• Guo , Y. , and DOOLEY , K. J. , 1992 , Identification of change structure in statistical process control . International Journal of Production Research , 30 ( 7 ), 1655 – 1669 .
   Web of Science ®Google Scholar
• HWARNG , H. B. , and HUBELE , N. F. , 1991 , X-bar chart pattern recognition using neural nets . Transactions of the ASQC Quality Congress , Milwaukee , WI , pp. 884 – 889 .
   Google Scholar
• HWARNG. H. B..HUBELE, N, F., 1992 , Boltzmann machines that learn to recognize patterns on control charts . Statistics and Computing , 2 ( 4 ), 191 – 202 .
   Google Scholar
• HWARNG , H. B. , and HUBELE , N. F. , 1993 a, Back-propagation pattern recognizers for [Xtilde] control charts methodology and performance . Computers and Industrial Engineering , 24 ( 2 ), 219 – 235 .
   Web of Science ®Google Scholar
• HWARNG , H. B. ,HUBELE. N. F., 1993b , [Xtilde] control chart pattern identification through efficient off-line neural network training . IIE Transactions , 25 ( 3 ), 27 – 40 .
   Web of Science ®Google Scholar
• HWARNG , H. B. , 1995 , Using harmonic amplitude statistics and various training strategies to improve the performance of a neural network in detecting cycles . IEE Transactions , in press .
   Google Scholar
• LIM , M. H. , GWEE , B. H. ,GOH.T. H., 1991 , Cause associator network for fuzzily deduced conclusion in process control. International Joint Conference on Neural Networks, Singapore, 2 , 1248 – 1253 .
   Google Scholar
• LIM , M. H. , and OOI , T. H. , 1990 , Statistical process control using fuzzy logic . British Institute of Non-Destructive Testing, Condition Monitoring and Diagnostic Technology , 1 ( 2 ), 64 – 70 .
   Google Scholar
• MONTGOMERY, D. C, 1991 , Introduction to statistical quality control ( New York Wiley ).
   Google Scholar
• PHAM , D. T. , and OZTEMEL , E. , 1992 , Control chart pattern recognition using neural networks . Journal of Systems Engineering , 2 ( 4 ), 256 – 262 .
   Google Scholar
• PHAM , D. T. , and OZTEMEL , E. , 1994 , Control chart pattern recognition using learner vector quantization networks . International Journal of Production Research , 32 ( 3 ), 721 – 729 .
   Web of Science ®Google Scholar
• RUMELHART , D. E. , HINTON , G. E. , and WILLIAMS , R. J. , 1986 , Learning internal representations by error propagation . In Parallel Distributed Processing Explorations in the Microstructure of Cognition , D. E. Rumelhart and J. L. McClelland (eds) ( Cambridge . MA Press ). pp. 318 – 362 .
   Google Scholar
• SAHRMANN, H.,; 1979 , Set-up assurance through time series analysis . Journal of Quality Technology , 11 , 105 – 115 .
   Web of Science ®Google Scholar
• SMITH , A. E. , 1994 , X-bar and R control chart interpretation using neural computing . International Journal of Production Research , 32 ( 2 ), 309 – 320 .
   Web of Science ®Google Scholar
• SPURRIER , J. D. , and THOMBS , L. A. , 1990 , Control charts for detecting cyclical behaviour . Technometrics , 32 ( 2 ), 163 – 171 .
   Web of Science ®Google Scholar
• WESTERN ELECTRIC CO., 1956 , Statistical Quality Control Handbook ( New York Western Electric ).
   Google Scholar