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| Application of Particle Swarm Algorithm and GMM-SDR Model in Overlapping Spectrum Peak Analysis |
| YANG Xi1, 2, HUANG Hong-quan1, 2*, JIANG Kai-ming1, CHEN Wen-de1, ZHOU Wei1, WANG Man-man1 |
1. College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610059, China
2. Fundamental Science on Radioactive Geology and Exploration Technology Laboratory, East China Institute of Technology，Nanchang 330013, China |
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Abstract In X-Ray spectrum analysis, the phenomenon of overlapping often occurs among spectrum peaks. In this paper, “Gaussian Mixture Model-Standard Deviation Related”(GMM-SDR) and Particle Swarm Optimization Algorithms were used for overlapping spectrum peak analysis. First, the GMM-SDR model of overlapping peaks was introduced, and the GMM-SDR parameters constitute the particle position. The objective function and the fast algorithm for the fitness function value were proposed. Secondly, the population search technology of particle swarm optimization algorithm was used to search the optimal GMM-SDR before decomposiing the overlapping peaks. In this algorithm, the initial value was set randomly and all measured random data were regarded as a whole. Since the probability matching degree of the model was taken as the fitness value, the method avoided the local convergence problem caused by improper initial value setting, and overcame the destruction of traditional curve fitting method to the original useful data. In fact, the model built with this method is a global optimal solution. The decomposition experiments of four showed high precision of the peak position, peak area and standard deviation with fewer overlapping peaks . The maximum relative error of decomposition of the two overlapping peaks was 0.4%, 0.05%, 2.07%, and which of the three overlapping peaks was 1.2%, 0.04% and 0.74%, respectively, which can be widely used for the decomposition of other overlapping peaks.
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Received: 2016-10-03
Accepted: 2017-02-15
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Corresponding Authors:
HUANG Hong-quan
E-mail: huanghongquan@cdut.cn
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