{"title":"准周期信号的可理解模型","authors":"A. Alvarez-Alvarez, G. Triviño","doi":"10.1109/ISDA.2009.65","DOIUrl":null,"url":null,"abstract":"In this paper we present a new method to analyze quasi-periodic signals. This method consists of modeling these signals using a Fuzzy Finite State Machine as a particular case of a Linguistic Fuzzy Model of a dynamical system. This model defines states and transitions using a priori knowledge of the signal we want to analyze. The model is represented using fuzzy rules that make it easily comprehensible. We include a practical example analyzing quasi-periodic signals of acceleration measured during the human gait cycle where good results were achieved.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Comprehensible Model of a Quasi-periodic Signal\",\"authors\":\"A. Alvarez-Alvarez, G. Triviño\",\"doi\":\"10.1109/ISDA.2009.65\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present a new method to analyze quasi-periodic signals. This method consists of modeling these signals using a Fuzzy Finite State Machine as a particular case of a Linguistic Fuzzy Model of a dynamical system. This model defines states and transitions using a priori knowledge of the signal we want to analyze. The model is represented using fuzzy rules that make it easily comprehensible. We include a practical example analyzing quasi-periodic signals of acceleration measured during the human gait cycle where good results were achieved.\",\"PeriodicalId\":330324,\"journal\":{\"name\":\"2009 Ninth International Conference on Intelligent Systems Design and Applications\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Ninth International Conference on Intelligent Systems Design and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISDA.2009.65\",\"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 Ninth International Conference on Intelligent Systems Design and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISDA.2009.65","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we present a new method to analyze quasi-periodic signals. This method consists of modeling these signals using a Fuzzy Finite State Machine as a particular case of a Linguistic Fuzzy Model of a dynamical system. This model defines states and transitions using a priori knowledge of the signal we want to analyze. The model is represented using fuzzy rules that make it easily comprehensible. We include a practical example analyzing quasi-periodic signals of acceleration measured during the human gait cycle where good results were achieved.