YANG Xiukun, ZHONG Mingliang, JING Xiaojun, WANG Jianli, CHEN Xuqi. NIR Chemical-Image Analysis Based on Adaptive Local Optimization PSO[J]. Chinese Journal of Electronics, 2014, 23(1): 115-118.
Citation: YANG Xiukun, ZHONG Mingliang, JING Xiaojun, WANG Jianli, CHEN Xuqi. NIR Chemical-Image Analysis Based on Adaptive Local Optimization PSO[J]. Chinese Journal of Electronics, 2014, 23(1): 115-118.

NIR Chemical-Image Analysis Based on Adaptive Local Optimization PSO

Funds:  This work is supported by the National Natural Science Foundation of China (No.61143008).
  • Received Date: 2012-11-01
  • Rev Recd Date: 2013-06-01
  • Publish Date: 2014-01-05
  • Principal component analysis (PCA) combined with cluster analysis has become an effective approach for Near-infrared (NIR) chemical image analysis. Traditional cluster algorithms are sensitive to initial starting conditions and can be trapped into local optimal solutions. To overcome the drawbacks, we develop a new algorithm in this paper which improves Particle swarm optimization with Adaptive local optimization (ALO-PSO). Simulation experiments performed on NIR image of tablet verify the feasibility and effectiveness of the proposed algorithm. Experimental results of the clustering performances indicate that ALO-PSO algorithm offers an alternative approach for solving data clustering problems in NIR chemical image analysis.
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