Research On The Technology Of Automatic Cotton-Blending And Yarn Prediction System Based On Artificial Intelligence

This paper discussed the design ideas of the computer distribution technique and yarn quality prediction system based on artificial intelligence, it also researched the issue that the data service layer, business logic layer and client application layer is separated and packaged to protect the data security and on the Browser/Server Mode. The method which is a much technical comprehensive application, involving the related knowledge of the computer software design and the Artificial Intelligence algorithm is discussed and it can implement the function of automatic cotton assorting and yarn quality prediction .And the MVC framework is implemented in the ASP.Net development platform.The relation between the cotton and yarn quality is studied. In order to solve the hard problems of computer automatic cotton-blending, it analyzes the advantages and weaknesses of the basic genetic algorithm in solving this problem. Then a new computer automatic cotton-blending model is designed. It proposes an improved hybrid genetic algorithm by using key techniques such as group ordering and local optimal methods. This paper designs the quality prediction model of yarns based on the BP neural network algorithm and RBF neural network algorithm, analyzes the defects and reasons for using standard BP neural network algorithm in building quality prediction model of yarns and explores an improved BP neural network algorithm. The experiment has proved that such model can increase the convergent speed of network and improve system stability. The quality prediction model of yarns based on genetic algorithm and RBF neural network algorithm can be a good solution to predict the quality of yarn for its accuracy and shorter training time.