Study of Adaptive Dynamic Search PSO Based SVM Parameter Optimization

Authors

Chunneng Gao, Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;
Zhang Biao, Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;
Zhicheng Ji, Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: According to critical control points (CCPs) selection problem in wheat processing HACCP (hazard analysis and critical control point), an automatic identification method based on SVM model was introduced. In order to improve the model’s recognition stability and accuracy, an adaptive dynamic search particle swarm optimization (ADS-PSO) for the optimization of kernel function parameters in SVM was proposed. ADS-PSO introduced an evolutionary factor and threshold (ET) to estimate the evolutionary state and adjusted the search strategy adaptively. Besides, an inertia parameter for the velocity was defined in ADS-PSO. The simulation results show that the improved SVM model can identify the CCPs in wheat processing HACCP, and achieve a high recognition accuracy and stability.

Recommended Citation

Gao, Chunneng; Biao, Zhang; and Ji, Zhicheng (2020) "Study of Adaptive Dynamic Search PSO Based SVM Parameter Optimization," Journal of System Simulation: Vol. 27: Iss. 12, Article 12.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss12/12

First Page

2958

Revised Date

2015-07-17

DOI Link

https://doi.org/

Last Page

2964

CLC

TP18

Recommended Citation

Gao Chunneng, Zhang Biao, Ji Zhicheng. Study of Adaptive Dynamic Search PSO Based SVM Parameter Optimization[J]. Journal of System Simulation, 2015, 27(12): 2958-2964.