{"title":"基于反向传播的模糊神经网络的改进","authors":"Qiang Hua, M. Ha","doi":"10.1109/ICMLC.2002.1175437","DOIUrl":null,"url":null,"abstract":"Some discussions on the fuzzy neural network architecture and algorithm have been put forward, whose weights are given as special fuzzy numbers, such as triangular fuzzy numbers. In this paper, we introduce the conception of strong L-R type fuzzy number, and derive a learning algorithm based on BP algorithm via level sets of strong L-R type fuzzy numbers. The special fuzzy number is weakened to the common case. Then the range of application is enlarged. Finally, the initial experiment in fuzzy classification is shown.","PeriodicalId":90702,"journal":{"name":"Proceedings. International Conference on Machine Learning and Cybernetics","volume":"23 1","pages":"2237-2239 vol.4"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The improvement of a fuzzy neural network based on backpropagation\",\"authors\":\"Qiang Hua, M. Ha\",\"doi\":\"10.1109/ICMLC.2002.1175437\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Some discussions on the fuzzy neural network architecture and algorithm have been put forward, whose weights are given as special fuzzy numbers, such as triangular fuzzy numbers. In this paper, we introduce the conception of strong L-R type fuzzy number, and derive a learning algorithm based on BP algorithm via level sets of strong L-R type fuzzy numbers. The special fuzzy number is weakened to the common case. Then the range of application is enlarged. Finally, the initial experiment in fuzzy classification is shown.\",\"PeriodicalId\":90702,\"journal\":{\"name\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"volume\":\"23 1\",\"pages\":\"2237-2239 vol.4\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2002.1175437\",\"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. International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2002.1175437","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The improvement of a fuzzy neural network based on backpropagation
Some discussions on the fuzzy neural network architecture and algorithm have been put forward, whose weights are given as special fuzzy numbers, such as triangular fuzzy numbers. In this paper, we introduce the conception of strong L-R type fuzzy number, and derive a learning algorithm based on BP algorithm via level sets of strong L-R type fuzzy numbers. The special fuzzy number is weakened to the common case. Then the range of application is enlarged. Finally, the initial experiment in fuzzy classification is shown.
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