{"title":"木材弹性模量估计的混合粒子群算法","authors":"Ming-Bao Li, Jiawei Zhang","doi":"10.1109/CIMSA.2009.5069959","DOIUrl":null,"url":null,"abstract":"Particle swarm optimization algorithm based neural network construction has been presented to calibrate the complex nonlinear relationship between modulus of elasticity (MOE) and wood physical property parameters. Consider that the traditional BP algorithm has shortcomings of converging slowly and easily trapping a local minimum value, a hybrid algorithm using particle swarm optimization (PSO) and back propagation (BP) is adopted to train the neural network. Modeling and Simulation results show that the optimization technique based on PSO modeling method is feasible and effective, with high generalization ability of the model and forecast accuracy.","PeriodicalId":178669,"journal":{"name":"2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"273 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid pso algorithm for estimation modulus of elasticity of wood\",\"authors\":\"Ming-Bao Li, Jiawei Zhang\",\"doi\":\"10.1109/CIMSA.2009.5069959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Particle swarm optimization algorithm based neural network construction has been presented to calibrate the complex nonlinear relationship between modulus of elasticity (MOE) and wood physical property parameters. Consider that the traditional BP algorithm has shortcomings of converging slowly and easily trapping a local minimum value, a hybrid algorithm using particle swarm optimization (PSO) and back propagation (BP) is adopted to train the neural network. Modeling and Simulation results show that the optimization technique based on PSO modeling method is feasible and effective, with high generalization ability of the model and forecast accuracy.\",\"PeriodicalId\":178669,\"journal\":{\"name\":\"2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications\",\"volume\":\"273 2\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-05-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIMSA.2009.5069959\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIMSA.2009.5069959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hybrid pso algorithm for estimation modulus of elasticity of wood
Particle swarm optimization algorithm based neural network construction has been presented to calibrate the complex nonlinear relationship between modulus of elasticity (MOE) and wood physical property parameters. Consider that the traditional BP algorithm has shortcomings of converging slowly and easily trapping a local minimum value, a hybrid algorithm using particle swarm optimization (PSO) and back propagation (BP) is adopted to train the neural network. Modeling and Simulation results show that the optimization technique based on PSO modeling method is feasible and effective, with high generalization ability of the model and forecast accuracy.