Journal of Systems Engineering and Electronics ›› 2010, Vol. 21 ›› Issue (3): 509-513.doi: 10.3969/j.issn.1004-4132.2010.03.024
• SOFTWARE ALGORITHM AND SIMULATION • Previous Articles Next Articles
Ying Li, Bendu Bai*, and Yanning Zhang
Online:
Published:
Abstract:
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.
Ying Li, Bendu Bai*, and Yanning Zhang. Improved particle swarm optimization algorithm for fuzzy multi-class SVM[J]. Journal of Systems Engineering and Electronics, 2010, 21(3): 509-513.
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URL: https://www.jseepub.com/EN/10.3969/j.issn.1004-4132.2010.03.024
https://www.jseepub.com/EN/Y2010/V21/I3/509