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| Application of Adaptive Artificial Bee Colony Optimization Extreme Learning Machine in Quantitative Analysis of Blood by Raman Spectroscopy |
| PIAN Fei-fei1,WANG Qiao-yun1,2,WANG Ming-xuan1,ZHANG Chu1,SHAN Peng1,2,LI Zhi-gang1,2 |
1. College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China
2. Hebei Province Key Laboratory of Micro-Nano Precision Optical Sensing and Detection Technology, Northeastern University at Qinhuangdao, Qinhuangdao, Hebei 066004, China |
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Abstract A method based on adaptive differential evolution artificial bee colony optimization extreme learning machine is proposed to predict the concentration of each component of blood. First, the artificial bee colony algorithm is used to iteratively optimize the input weights and hidden layer thresholds; secondly, the differential evolution is combined to further improve the model accuracy and avoid problems such as falling into local optimality in the later stage; due to the fact that the crossover rate and mutation rate of the differential evolution algorithm are based on experience, the idea of adaptive adjustment is introduced, and the model of adaptive differential evolution artificial bee colony algorithm to optimize the extreme learning machine algorithm is proposed and applied to the quantitative analysis of blood components. Experiments show that the extreme learning machine model optimized by the adaptive differential evolution artificial bee colony algorithm has high prediction accuracy, and the model has strong robustness.
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Received: 26 January 2021
Published: 21 February 2023
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2023, Vol. 44 
