Predicting fabric weight per unit area of knitted structure using fuzzy logic systems.

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dc.contributor.author Abaho, Vicent
dc.date.accessioned 2022-05-14T08:18:36Z
dc.date.available 2022-05-14T08:18:36Z
dc.date.issued 2015-05
dc.identifier.citation Abaho, Vicent. (2015). Predicting fabric weight per unit area of knitted structure using fuzzy logic systems. Busitema University. Unpublished dissertation. en_US
dc.identifier.uri http://hdl.handle.net/20.500.12283/1133
dc.description Dissertation en_US
dc.description.abstract The main purpose of this study is to develop a fuzzy model for the prediction of fabric GSM of cotton knitted fabrics as a function of yarn count and knitting stitch length. The most significant key factors yarn count and knitting stitch length affect the GSM of cotton knitted fabrics are very non-linear. Hence, developing a prediction model by using mathematical or statistical approach is very difficult task. Conversely, artificial neural network and neural-fuzzy models are trained using vast amounts of experimental data which are also time consuming process. Fuzzy logic (FL) methodology on the other hand, is a promising modelling tool which performs extraordinarily very robust in a non-linear complex field with least trial data. The prediction model was found to be valid by correlation analysis. The mean relative error, correlation coefficient and goodness of fit from the predicted values were found to be 4.56%, 0.995 and 0.999 respectively. The results show very good performance of the prediction model. en_US
dc.description.sponsorship Dr. Nibikora Ildephonse, Busitema University. en_US
dc.language.iso en en_US
dc.publisher Busitema University. en_US
dc.subject Fabric weight en_US
dc.subject Knitted structure en_US
dc.subject Fuzzy logic systems en_US
dc.subject Fuzzy model en_US
dc.subject Yarn en_US
dc.subject Knitted fabrics en_US
dc.title Predicting fabric weight per unit area of knitted structure using fuzzy logic systems. en_US
dc.type Thesis en_US


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