Research on Feature Selection Based on Improved Particle Swarm Optimization
Feature selection is one of key technologies for fault diagnosis. Especially for high dimensional data, Feature selection can not only find the feature subset with sufficient information, but also improve the classification accuracy and efficiency. In order to decrease the number of diagnosis parameter in fault diagnosis of Liquid-propellant Rocket Engine, the paper proposes one feature selection method based on improved particle swarm optimization, the method applies the quantum evolution thoughts to PSO. The particle is restricted in the range from -π/2 to 0, so the particle can correspond to the quantum angle. The parameter optimization function is designed. The improved algorithm can decrease the number of parameter in fault diagnosis of Liquid-propellant Rocket Engine from 25 to 6.
Liangchi Zhang, Chunliang Zhang, Jeng-Haur Horng and Zichen Chen
G. Q. Wang et al., "Research on Feature Selection Based on Improved Particle Swarm Optimization", Advanced Materials Research, Vols. 591-593, pp. 2651-2654, 2012