Wound Infection Recognition Based on Quantum-behaved Particle Swarm Optimization
This study uses an electronic nose including 20 sensors to recognize the pathogen species of male rats, and each rat is injected with three kinds of pathogens respectively.How to remove the redundancy and correlation among sensors is the key to wound diagnosis.Traditional feature selection algorithm is complex especially in medical electronic nose.In this paper, a feature selection method based on binary quantum-behaved particle swarm optimization (BQPSO) algorithm is proposed, and after the feature selection, genetic quantum-behaved particle swarm optimization (GQPSO) is used to develop a synchronous optimization of sensor array and classifier parameters.Radial basis function neural network (RBFNN) combined with BQPSO and GQPSO is used to select proper sensors and optimize the RBF parameters and sensor array synchronously.Experimental results indicate that this method is effective and remarkable on the recognition of rats wound infection based on electronic nose.
Wound infection Feature selection Quantum-behaved particle swarm optimization Electronic nose Synchronous optimization
Shu Fan Fengchun Tian Qinghua He pengfei Jia Jingwei Feng Yue Shen
College of Communication Engineering, Chongqing University, 400030 State Key Laboratory of Trauma, Bums and Combined Injury, Institute of Surgery Research, Daping Hosp
国内会议
广州
英文
496-507
2016-09-01（万方平台首次上网日期，不代表论文的发表时间）