{"title":"基于部落粒子群优化的神经模糊推理系统预测太阳黑子数","authors":"Cheng-Hung Chen, Yen-Yun Liao, Shu-Wei Liu","doi":"10.1109/ICICS.2013.6782878","DOIUrl":null,"url":null,"abstract":"This study presents tribal particle swarm optimization (TPSO) to optimize the parameters of the specific neurofuzzy inference system (NIS) for forecasting sunspot numbers. The proposed TPSO uses particle swarm optimization (PSO) as evolution strategies of the tribes optimization algorithm (TOA) to balance local and global exploration of the search space. Experimental results demonstrated that the proposed TPSO method converges quickly and yields a lower RMS error than other current methods.","PeriodicalId":184544,"journal":{"name":"2013 9th International Conference on Information, Communications & Signal Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neurofuzzy inference systems based on tribal particle swarm optimization for forecasting sunspot numbers\",\"authors\":\"Cheng-Hung Chen, Yen-Yun Liao, Shu-Wei Liu\",\"doi\":\"10.1109/ICICS.2013.6782878\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study presents tribal particle swarm optimization (TPSO) to optimize the parameters of the specific neurofuzzy inference system (NIS) for forecasting sunspot numbers. The proposed TPSO uses particle swarm optimization (PSO) as evolution strategies of the tribes optimization algorithm (TOA) to balance local and global exploration of the search space. Experimental results demonstrated that the proposed TPSO method converges quickly and yields a lower RMS error than other current methods.\",\"PeriodicalId\":184544,\"journal\":{\"name\":\"2013 9th International Conference on Information, Communications & Signal Processing\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 9th International Conference on Information, Communications & Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICS.2013.6782878\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 9th International Conference on Information, Communications & Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICS.2013.6782878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neurofuzzy inference systems based on tribal particle swarm optimization for forecasting sunspot numbers
This study presents tribal particle swarm optimization (TPSO) to optimize the parameters of the specific neurofuzzy inference system (NIS) for forecasting sunspot numbers. The proposed TPSO uses particle swarm optimization (PSO) as evolution strategies of the tribes optimization algorithm (TOA) to balance local and global exploration of the search space. Experimental results demonstrated that the proposed TPSO method converges quickly and yields a lower RMS error than other current methods.