Research of Tent Map Based Chaotic Particle Swarm Optimization Algorithm for Emotion Recognition

Abstract:

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For the problem of feature redundancy of emotion recognition based on multi-channel physiological signals and low efficiency of traditional feature reduction algorithms on great sample data, a new chaotic particle swarm optimization algorithm (TM-CPSO) was proposed to solve the problem of emotion feature selection by combining tent map based chaos search mechanism and improved particle swarm optimization algorithm. The problem of falling into local minimum can be avoided by mapping the search process to the recursive procedure of the chaotic orbit. The recognition rate and efficiency was increased and the algorithm's validity was verified through the analysis of experimental simulation data and the comparison of several recognition methods.

Info:

Periodical:

Advanced Materials Research (Volumes 143-144)

Edited by:

H. Wang, B.J. Zhang, X.Z. Liu, D.Z. Luo, S.B. Zhong

Pages:

1280-1284

DOI:

10.4028/www.scientific.net/AMR.143-144.1280

Citation:

H. N. Wang et al., "Research of Tent Map Based Chaotic Particle Swarm Optimization Algorithm for Emotion Recognition", Advanced Materials Research, Vols. 143-144, pp. 1280-1284, 2011

Online since:

October 2010

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Price:

$35.00

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