Study of Adaptive Dynamic Search PSO Based SVM Parameter Optimization

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.

Key words: HACCP, support vector machine, adaptively dynamic search particle swarm optimization, wheat processing

CLC Number:

TP18

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.

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