The Prediction of Yarn Elongation of Kenyan Ring-Spun Yarn using Extreme Learning Machines (ELM)

Abstract

The optimization of the manufacture of cotton yarns involves several processes, while the prediction of yarn quality parameters forms an important area of investigation. This research work concentrated on the pre- diction of cotton yarn elongation. Cotton lint and yarn samples were collected in textile factories in Kenya. The collected samples were tested under standard testing conditions. Cotton lint parameters, machine pa- rameters and yarn elongation were used to design yarn elongation prediction models. The elongation pre- diction models used three network training algorithms, including backpropagation (BP), an extreme learn- ing machine (ELM), and a hybrid of diff erential evolution (DE) and an ELM referred to as DE-ELM. The prediction models recorded a mean squared error (mse) value of 0.001 using 11, 43 and 2 neurons in the hidden layer for the BP, ELM and DE-ELM models respectively. The ELM models exhibited faster training speeds than the BP algorithms, but required more neurons in the hidden layer than other models. The DE- ELM hybrid algorithm was faster than the BP algorithm, but slower than the ELM algorithm. Keywords: cotton yarn, elongation, backpropagation, extreme learning machines, prediction