A prediction method using extreme learning machine with immune optimization
Jin‐Xi Zhang, Yongsheng Ding, Kuangrong Hao, Lei Chen, Lihong Ren · 2017
A prediction method based on Extreme Learning Machines(ELM) is proposed. The parameters of ELM are optimized by immune optimization algorithm. The proposed model is used to predict the fiber quality of the melt spinning process. According to the limited samples, the spinning speed, the spinning temperature, the blowing speed and the blowing temperature of the melt spinning process are four key parameters that affect the fiber quality. The four indexes of polyester fiber are 1.5 times rate of elongation(EYS1.5), elongation inequality rate(EYSCV), breaking strength(DT) and elongation capacity(DE). We collect actual data samples in the factory and establish the ELM prediction model. The ELM model optimized by immune optimization algorithm (ELM-IA) is compared with PSO optimized ELM (ELM-PSO), ELM model and BP neural network. The experimental results show that the ELM-IA model is better than the ELM-PSO model, the ELM model and BP neural network in prediction. The ELM-IA model has a good generalization ability. It can play a guiding role in the actual fiber production process.