NIR Chemical-Image Analysis Based on Adaptive Local Optimization PSO

YANG Xiukun; ZHONG Mingliang; JING Xiaojun; WANG Jianli; CHEN Xuqi

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Article Navigation > Chinese Journal of Electronics > 2014 > 23(1): 115-118

YANG Xiukun, ZHONG Mingliang, JING Xiaojun, et al., “NIR Chemical-Image Analysis Based on Adaptive Local Optimization PSO,” Chinese Journal of Electronics, vol. 23, no. 1, pp. 115-118, 2014,

Citation:

YANG Xiukun, ZHONG Mingliang, JING Xiaojun, et al., “NIR Chemical-Image Analysis Based on Adaptive Local Optimization PSO,” Chinese Journal of Electronics, vol. 23, no. 1, pp. 115-118, 2014,

YANG Xiukun, ZHONG Mingliang, JING Xiaojun, et al., “NIR Chemical-Image Analysis Based on Adaptive Local Optimization PSO,” Chinese Journal of Electronics, vol. 23, no. 1, pp. 115-118, 2014,

Citation:

YANG Xiukun, ZHONG Mingliang, JING Xiaojun, et al., “NIR Chemical-Image Analysis Based on Adaptive Local Optimization PSO,” Chinese Journal of Electronics, vol. 23, no. 1, pp. 115-118, 2014,

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NIR Chemical-Image Analysis Based on Adaptive Local Optimization PSO

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China;
2.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China

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

Abstract

Abstract

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.
- Near-infrared chemical image,
- Principal component analysis,
- Cluster analysis,
- Particle swarm optimization (PSO),
- Adaptive local optimization (ALO)

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References(12)

References

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