{"title":"优先顺序方向基函数神经网络及其在目标识别中的应用","authors":"Wenming Cao, Fei Lu, Shoujue Wang","doi":"10.1109/GRC.2006.1635788","DOIUrl":null,"url":null,"abstract":"A brand new architecture of neural networks has been introduced, In this architecture, outputs of direction basis function neurons[1] are with different priorities. It has been discussed that the Priority Ordered Direction Basis Function Neural Network (PODBFNN). The Priority Ordered Direction Basis Function Nets (PODBFN) for object recognition has been analyzed. The experiment shows that the learning speed of the PODBFN are much faster than that of the multilayered feedforward neural networks with existing BP algorithms.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Priority ordered direction basis function neural networks and the application for object recognition\",\"authors\":\"Wenming Cao, Fei Lu, Shoujue Wang\",\"doi\":\"10.1109/GRC.2006.1635788\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A brand new architecture of neural networks has been introduced, In this architecture, outputs of direction basis function neurons[1] are with different priorities. It has been discussed that the Priority Ordered Direction Basis Function Neural Network (PODBFNN). The Priority Ordered Direction Basis Function Nets (PODBFN) for object recognition has been analyzed. The experiment shows that the learning speed of the PODBFN are much faster than that of the multilayered feedforward neural networks with existing BP algorithms.\",\"PeriodicalId\":400997,\"journal\":{\"name\":\"2006 IEEE International Conference on Granular Computing\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE International Conference on Granular Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GRC.2006.1635788\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE International Conference on Granular Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GRC.2006.1635788","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Priority ordered direction basis function neural networks and the application for object recognition
A brand new architecture of neural networks has been introduced, In this architecture, outputs of direction basis function neurons[1] are with different priorities. It has been discussed that the Priority Ordered Direction Basis Function Neural Network (PODBFNN). The Priority Ordered Direction Basis Function Nets (PODBFN) for object recognition has been analyzed. The experiment shows that the learning speed of the PODBFN are much faster than that of the multilayered feedforward neural networks with existing BP algorithms.
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