Xin-Yi Tsai, Yu-Ju Chen, Huang-Chu Huang, Shang-Jen Chuang, R. Hwang
{"title":"量子神经网络与神经网络在信号识别中的对比","authors":"Xin-Yi Tsai, Yu-Ju Chen, Huang-Chu Huang, Shang-Jen Chuang, R. Hwang","doi":"10.1109/ICITA.2005.228","DOIUrl":null,"url":null,"abstract":"In this paper, the signal recognition by using quantum neural network (QNN) is studied and simulated. The signals with fuzziness distributed in the boundary of two different types of signals could be effectively recognized due to the structure of QNN's hidden units. To demonstrate the capability of QNN in recognition, the signals in a two-dimension (NC2) non-convex system is simulated. All the experiments are also performed by using the traditional neural network (NN) for a comparison.","PeriodicalId":371528,"journal":{"name":"Third International Conference on Information Technology and Applications (ICITA'05)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Quantum NN vs. NN in signal recognition\",\"authors\":\"Xin-Yi Tsai, Yu-Ju Chen, Huang-Chu Huang, Shang-Jen Chuang, R. Hwang\",\"doi\":\"10.1109/ICITA.2005.228\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the signal recognition by using quantum neural network (QNN) is studied and simulated. The signals with fuzziness distributed in the boundary of two different types of signals could be effectively recognized due to the structure of QNN's hidden units. To demonstrate the capability of QNN in recognition, the signals in a two-dimension (NC2) non-convex system is simulated. All the experiments are also performed by using the traditional neural network (NN) for a comparison.\",\"PeriodicalId\":371528,\"journal\":{\"name\":\"Third International Conference on Information Technology and Applications (ICITA'05)\",\"volume\":\"106 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Third International Conference on Information Technology and Applications (ICITA'05)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICITA.2005.228\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Third International Conference on Information Technology and Applications (ICITA'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICITA.2005.228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, the signal recognition by using quantum neural network (QNN) is studied and simulated. The signals with fuzziness distributed in the boundary of two different types of signals could be effectively recognized due to the structure of QNN's hidden units. To demonstrate the capability of QNN in recognition, the signals in a two-dimension (NC2) non-convex system is simulated. All the experiments are also performed by using the traditional neural network (NN) for a comparison.
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