{"title":"模糊Petri网","authors":"P. Cheng, K. Forward","doi":"10.1109/KES.1997.619416","DOIUrl":null,"url":null,"abstract":"Proposes a new model of a fuzzy Petri net and an algorithm to generate such a network automatically. As an example of the application of the fuzzy Petri net, it is used to classify the Iris data set. Although there are extensive examples of neural network-based classifiers in the literature, they all share the undesirable characteristic of a long learning time. We attempt to remedy this problem by using a totally different architecture, and the resulting Petri net attains comparable performance with conventional systems with just a few seconds of training.","PeriodicalId":166931,"journal":{"name":"Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Fuzzy Petri nets\",\"authors\":\"P. Cheng, K. Forward\",\"doi\":\"10.1109/KES.1997.619416\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Proposes a new model of a fuzzy Petri net and an algorithm to generate such a network automatically. As an example of the application of the fuzzy Petri net, it is used to classify the Iris data set. Although there are extensive examples of neural network-based classifiers in the literature, they all share the undesirable characteristic of a long learning time. We attempt to remedy this problem by using a totally different architecture, and the resulting Petri net attains comparable performance with conventional systems with just a few seconds of training.\",\"PeriodicalId\":166931,\"journal\":{\"name\":\"Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97\",\"volume\":\"64 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/KES.1997.619416\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KES.1997.619416","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Proposes a new model of a fuzzy Petri net and an algorithm to generate such a network automatically. As an example of the application of the fuzzy Petri net, it is used to classify the Iris data set. Although there are extensive examples of neural network-based classifiers in the literature, they all share the undesirable characteristic of a long learning time. We attempt to remedy this problem by using a totally different architecture, and the resulting Petri net attains comparable performance with conventional systems with just a few seconds of training.