| Citation: | ZHOU Meiling, ZHENG Dezhi, LOU Ge, et al. Quantitative spectral modeling method based on improved extreme learning machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(6): 1208-1215. doi: 10.13700/j.bh.1001-5965.2016.0459(in Chinese)
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According to the principle of near infrared spectrum (NIR), an optimized extreme learning machine algorithm with particle swarm optimization (PSO-ELM) was proposed and used in ammonia concentration quantitative analysis of small sample. By optimizing the hidden node parameters of extreme learning machine (ELM) algorithm, the problem of randomly generated input weight and hidden layer of ELM leading to random modeling results has been solved. At the same time, the model stability, accuracy and generalization performance were improved. Through the experimental verification, by the optimized PSO-ELM, compared to ELM, the root mean square error of prediction set reduces to 0.003 22 from 0.011 66 and the correlation coefficient of prediction increases from 0.995 1 to 0.997 9. After comparing the optimized model prediction results with the modeling results of traditional support vector machine (SVM) regression and BP neural network algorithm, optimized PSO-ELM offers high accuracy and excellent generalization performance. Model prediction effect is superior to the traditional quantitative regression analysis algorithm.
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