{"title":"随机环境下基于模糊知识的诊断任务方法","authors":"A. Walaszek-Babiszewska","doi":"10.1109/CINTI.2013.6705237","DOIUrl":null,"url":null,"abstract":"This work deals with the creating probabilistic-fuzzy knowledge-based systems by using the theory of fuzzy systems as well as the probability and stochastic processes theory. We show that such systems can be applied in different diagnostic tasks. The structure of the reason-result fuzzy model has a form of weighted rules. Weights represent empirical probabilities of fuzzy statements in antecedents and consequent parts of rules. The calculated probabilities of fuzzy events are included into inference/forecast procedures.","PeriodicalId":439949,"journal":{"name":"2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Fuzzy knowledge-based approach to diagnosis tasks in stochastic environment\",\"authors\":\"A. Walaszek-Babiszewska\",\"doi\":\"10.1109/CINTI.2013.6705237\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work deals with the creating probabilistic-fuzzy knowledge-based systems by using the theory of fuzzy systems as well as the probability and stochastic processes theory. We show that such systems can be applied in different diagnostic tasks. The structure of the reason-result fuzzy model has a form of weighted rules. Weights represent empirical probabilities of fuzzy statements in antecedents and consequent parts of rules. The calculated probabilities of fuzzy events are included into inference/forecast procedures.\",\"PeriodicalId\":439949,\"journal\":{\"name\":\"2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)\",\"volume\":\"171 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CINTI.2013.6705237\",\"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 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINTI.2013.6705237","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy knowledge-based approach to diagnosis tasks in stochastic environment
This work deals with the creating probabilistic-fuzzy knowledge-based systems by using the theory of fuzzy systems as well as the probability and stochastic processes theory. We show that such systems can be applied in different diagnostic tasks. The structure of the reason-result fuzzy model has a form of weighted rules. Weights represent empirical probabilities of fuzzy statements in antecedents and consequent parts of rules. The calculated probabilities of fuzzy events are included into inference/forecast procedures.
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