| Autor: |
Luo, Ruiling, Cai, Wenqing, Chen, Min, Zhu, Dongqin |
| Zdroj: |
2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI); 1/ 1/2012, p641-644, 4p |
| Abstrakt: |
Considering the Particle Swam Optimization (PSO) is easily relapsing into local extremum, an improved PSO(IPSO) is proposed in this paper. In the new algorithm, we apply the evolution speed factor as the trigger conditions to stochastically disturb the local optimal solution. The IPSO algorithm can not only improve extraordinarily the convergence velocity in the evolutionary optimization, but also can adjust the balance between global and local exploration suitably. Then a speaker recognition approach using this improved algorithm to train Support vector machine (SVM) is presented. The experimental results show that the SVM optimized by IPSO achieves higher classification accuracy than the standard SVM and effectively improves the speaker identification speed and accuracy. [ABSTRACT FROM PUBLISHER] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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