Analysis Of Fuzzy Clustering Of Sea Lan Cotton

Today most of the textile mill is based on the grade classification of cotton, and cotton grade is based on national standards, the use of integrated set such as touch, visual methods. In the classification process, this test method is prone to human error, thus leading to the rating unaccurate, with no guarantee in the process with a reasonable selection of menkwa cotton and the continuity of production and yarn quality.In order to carry out scientific and rational classification of menkwa cotton, there must be accurate indicators of value of cotton and use of scientific and reasonable classification. With the Uster HVI's system (high-capacity fiber tester) to test menkwa cotton, the indicators of the menkwa cotton is fully accurate and objectivey examined, excluding the human factors. The difference the required yarn quality of the raw is the cotton indicators are also not the same. Therefor, all kinds of menkwa cotton spinning performance between the boundaries are blurred and the classification of cotton must be accompanied to ambiguity. According to that principle, classification of menkwa cotton must use the fuzzy C clustering method and gray Fuzzy equivalent matrix method of fuzzy mathematics. Fuzzy clustering of computational classification of menkwa cotton is large and some parameters must be determined artificially. We use the C # development of the cotton cluster system which is simple easy to operate.Finally, the result of clssification is obtained from the use of fuzzy clustering in Xinjiang textile mill in accordence with detected indicators of HVI data of the Sea Island Cotton. The results is better than using grade classification, better reflecting the performance of cotton spinning, which can guide the distribution of cotton, superior cotton for better use and ensure production continuity and quality of spinning.