In the past years. neuro-fiuy systems received an increasing attention and were used to solve a wide range of problelix in different domains. A ne~u-o-fiwq S~S~CIII is a hybrid system consisting of an artificial neural network and a fi~zzy inference system where the learning algorithm of the artificial neural network is ilsed lo adjust the parameters of the membership functions associated with the fuzzy inference system. This paper proposes a neuro-fi~uy classification approach for identifying control chart patterns in order to uncover the behavior of the production process. The proposed approach was implemented by building a neuro-f~wy classification system and b\z using simulated data. Nunierical results showed that the proposcd approach has a good recognition periormancc of patterns on control charts.
Al-Hindi, H. A. (2001). A NEURO-FUZZY CLASSIFICATION SYSTEM FOR PATTERN RECOGNlTION OF CONTROL CHARTS. ERJ. Engineering Research Journal, 24(3), 53-67. doi: 10.21608/erjm.2001.71049
MLA
Hindi A. Al-Hindi. "A NEURO-FUZZY CLASSIFICATION SYSTEM FOR PATTERN RECOGNlTION OF CONTROL CHARTS". ERJ. Engineering Research Journal, 24, 3, 2001, 53-67. doi: 10.21608/erjm.2001.71049
HARVARD
Al-Hindi, H. A. (2001). 'A NEURO-FUZZY CLASSIFICATION SYSTEM FOR PATTERN RECOGNlTION OF CONTROL CHARTS', ERJ. Engineering Research Journal, 24(3), pp. 53-67. doi: 10.21608/erjm.2001.71049
VANCOUVER
Al-Hindi, H. A. A NEURO-FUZZY CLASSIFICATION SYSTEM FOR PATTERN RECOGNlTION OF CONTROL CHARTS. ERJ. Engineering Research Journal, 2001; 24(3): 53-67. doi: 10.21608/erjm.2001.71049
)
View on SCiNiTO