{"title":"基于GA-PSO算法的压电换能器电路模型参数估计","authors":"Tao Liu, Yongyong Duan, Zongmei Bai","doi":"10.1109/ICMIC.2018.8529949","DOIUrl":null,"url":null,"abstract":"The parameters estimation problem for the equivalent admittance circuit model of piezoelectric transducers with high non-linearity near the resonance frequency is studied. A novel Genetic-Particle Swarm Optimization (GA-PSO) algorithm with cascade structure is proposed. The variance of fitness value of population as a criterion is given to evaluate the population convergence, and the population convergence is used as an adaptive switching strategy of Particle Swarm Optimization(PSO) to Genetic Algorithm(GA). The proposed GA-PSO method can also be used for estimating parameters of others. Simulation results are provided to demonstrate the feasibility. Comparison of the proposed GA-PSO method with the existing method is given, which shows that the GA-PSO alaorithm is more efficient.","PeriodicalId":262938,"journal":{"name":"2018 10th International Conference on Modelling, Identification and Control (ICMIC)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Parameter Estimation of Piezoelectric Transducers Circuit Model Using GA-PSO Algorithm\",\"authors\":\"Tao Liu, Yongyong Duan, Zongmei Bai\",\"doi\":\"10.1109/ICMIC.2018.8529949\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The parameters estimation problem for the equivalent admittance circuit model of piezoelectric transducers with high non-linearity near the resonance frequency is studied. A novel Genetic-Particle Swarm Optimization (GA-PSO) algorithm with cascade structure is proposed. The variance of fitness value of population as a criterion is given to evaluate the population convergence, and the population convergence is used as an adaptive switching strategy of Particle Swarm Optimization(PSO) to Genetic Algorithm(GA). The proposed GA-PSO method can also be used for estimating parameters of others. Simulation results are provided to demonstrate the feasibility. Comparison of the proposed GA-PSO method with the existing method is given, which shows that the GA-PSO alaorithm is more efficient.\",\"PeriodicalId\":262938,\"journal\":{\"name\":\"2018 10th International Conference on Modelling, Identification and Control (ICMIC)\",\"volume\":\"150 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 10th International Conference on Modelling, Identification and Control (ICMIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMIC.2018.8529949\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 10th International Conference on Modelling, Identification and Control (ICMIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMIC.2018.8529949","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parameter Estimation of Piezoelectric Transducers Circuit Model Using GA-PSO Algorithm
The parameters estimation problem for the equivalent admittance circuit model of piezoelectric transducers with high non-linearity near the resonance frequency is studied. A novel Genetic-Particle Swarm Optimization (GA-PSO) algorithm with cascade structure is proposed. The variance of fitness value of population as a criterion is given to evaluate the population convergence, and the population convergence is used as an adaptive switching strategy of Particle Swarm Optimization(PSO) to Genetic Algorithm(GA). The proposed GA-PSO method can also be used for estimating parameters of others. Simulation results are provided to demonstrate the feasibility. Comparison of the proposed GA-PSO method with the existing method is given, which shows that the GA-PSO alaorithm is more efficient.