Comparison of LMBP and Lmbp Hybrid algorithms during the prediction of Ring Spun Yarn Evvenness

Abstract

Yarn evenness is one of the important yarn quality parameters, which affects the subsequent pro- cessing and usage of the yarn. This research work concentrated on the study of Kenyan ring spun yarn evenness. Cotton and yarn samples were collected from factories in Kenya. The data collected from the samples was used to design yarn evenness predic- tion models. The yarn evenness prediction models were trained using Levenberg-Marquardt Backpropa- gation algorithm (LMBP), one of the most commonly used algorithms in the prediction of yarn properties. This research work went a step further and attempt- ed to improve the working of the evenness prediction models using hybrid algorithms, namely Differential Evolution and LMBP (LMBP-DE) and LMBP and Particle Swam Optimization (PSO), christened LMBP -PSO. A comparison of the performance of the yarn unevenness prediction algorithms as the neurons in the hidden layer were varied from 2 to 16 in steps of twos was undertaken. In terms of speed (training time) and performance (mse value) the hybrid algo- rithms performed better than the LMBP algorithm. LMBP also showed a higher CV of mse values which could imply that it was more prone to getting stuck in local minima than the hybrid algorithms.