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Improved particle swarm optimization algorithm for fuzzy multi-class SVM 被引量：17

Improved particle swarm optimization algorithm for fuzzy multi-class SVM
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摘要 An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from itself and the best one but also from the mean value of some other particles.In addition,adaptive mutation was introduced to reduce the rate of premature convergence.The experimental results on the synthetic aperture radar（SAR） target recognition of moving and stationary target acquisition and recognition（MSTAR） dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training. An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from itself and the best one but also from the mean value of some other particles.In addition,adaptive mutation was introduced to reduce the rate of premature convergence.The experimental results on the synthetic aperture radar（SAR） target recognition of moving and stationary target acquisition and recognition（MSTAR） dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training.
作者 Ying Li Bendu Bai Yanning Zhang
机构地区 School of Computer Science
出处 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第3期509-513,共5页 系统工程与电子技术（英文版）
基金 supported by the National Natural Science Foundation of China (60873086) the Aeronautical Science Foundation of China(20085153013) the Fundamental Research Found of Northwestern Polytechnical Unirersity (JC200942)
关键词 particle swarm optimization（PSO） fuzzy support vector machine（FSVM） adaptive mutation multi-classification. particle swarm optimization（PSO）,fuzzy support vector machine（FSVM）,adaptive mutation,multi-classification.
分类号 TP391.43 [自动化与计算机技术—计算机应用技术] TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献15

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引证文献17

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Journal of Systems Engineering and Electronics
2010年 第3期

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