{"title":"利用统计参数进行混沌检测","authors":"K. Vibe-Rheymer, J. Vesin","doi":"10.1109/DSPWS.1996.555574","DOIUrl":null,"url":null,"abstract":"Detecting chaos in experimental data is a nontrivial problem. Nowadays, most techniques require long data sets and a low amount of noise in the data, which is not always possible. Besides, the results often leave much room to interpretation. The paper proposes an alternative to classical methods, using statistical techniques. The chaos detection test is decomposed into two sub-tests, detecting respectively the presence of fractality and nonlinearity in the signal. Several possible tests for each feature are presented and analyzed; the best combination test is then proposed.","PeriodicalId":131323,"journal":{"name":"1996 IEEE Digital Signal Processing Workshop Proceedings","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Using statistical parameters for chaos detection\",\"authors\":\"K. Vibe-Rheymer, J. Vesin\",\"doi\":\"10.1109/DSPWS.1996.555574\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Detecting chaos in experimental data is a nontrivial problem. Nowadays, most techniques require long data sets and a low amount of noise in the data, which is not always possible. Besides, the results often leave much room to interpretation. The paper proposes an alternative to classical methods, using statistical techniques. The chaos detection test is decomposed into two sub-tests, detecting respectively the presence of fractality and nonlinearity in the signal. Several possible tests for each feature are presented and analyzed; the best combination test is then proposed.\",\"PeriodicalId\":131323,\"journal\":{\"name\":\"1996 IEEE Digital Signal Processing Workshop Proceedings\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1996 IEEE Digital Signal Processing Workshop Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSPWS.1996.555574\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1996 IEEE Digital Signal Processing Workshop Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSPWS.1996.555574","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detecting chaos in experimental data is a nontrivial problem. Nowadays, most techniques require long data sets and a low amount of noise in the data, which is not always possible. Besides, the results often leave much room to interpretation. The paper proposes an alternative to classical methods, using statistical techniques. The chaos detection test is decomposed into two sub-tests, detecting respectively the presence of fractality and nonlinearity in the signal. Several possible tests for each feature are presented and analyzed; the best combination test is then proposed.
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