Volterra series identification method based on quantum particle swarm optimization

Li Zhinong; Jiang Jing; Chen Jingang; Wu Guanhua; Li Xuejun

Journal of Vibration and Shock ›› 2013, Vol. 32 ›› Issue (3) : 60-63.

PDF(1167 KB)
PDF(1167 KB)
Journal of Vibration and Shock ›› 2013, Vol. 32 ›› Issue (3) : 60-63.
论文

Volterra series identification method based on quantum particle swarm optimization

  • Li Zhinong1, Jiang Jing2, Chen Jingang2, Wu Guanhua1, Li Xuejun3
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Abstract

The quantum particle swarm optimization (QPSO) algorithm is introduced into the nonlinear Volterra system identification, a new Volterra series identification method based on the QPSO is proposed. In the proposed method, the QPSO algorithm is used to estimate nonlinear system Volterra kernel function. The proposed method is compared with traditional least mean square (LMS) identification method. The simulation result shows that the two methods have good identification precision and convergence under free-noise interference. However, under noise environment, whether in identification precision, convergence or anti-interference, the proposed method is superior to the traditional LMS identification method, especially at signal-to-noise ratio (SNR) is very small.

Key words

quantum particle swarm optimization (QPSO) / Volterra series / nonlinear system identification.

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Li Zhinong; Jiang Jing; Chen Jingang; Wu Guanhua; Li Xuejun. Volterra series identification method based on quantum particle swarm optimization[J]. Journal of Vibration and Shock, 2013, 32(3): 60-63
PDF(1167 KB)

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