{"title":"BP神经网络可以对图像进行智能识别","authors":"Su Zong","doi":"10.1117/12.2674863","DOIUrl":null,"url":null,"abstract":"Aiming at the disadvantages of slow convergence speed and unable to reduce recognition errors quickly, BP neural network is used for intelligent recognition of computer images. On this basis, the image recognition model is established by using BP neural network, and its modeling and modeling are carried out. Under the same experimental conditions, different recognition algorithms are compared. The results show that this algorithm has a better convergence speed, can effectively reduce errors, and can effectively improve the recognition rate of images.","PeriodicalId":286364,"journal":{"name":"Conference on Computer Graphics, Artificial Intelligence, and Data Processing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"BP neural network can recognize the image intelligently\",\"authors\":\"Su Zong\",\"doi\":\"10.1117/12.2674863\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the disadvantages of slow convergence speed and unable to reduce recognition errors quickly, BP neural network is used for intelligent recognition of computer images. On this basis, the image recognition model is established by using BP neural network, and its modeling and modeling are carried out. Under the same experimental conditions, different recognition algorithms are compared. The results show that this algorithm has a better convergence speed, can effectively reduce errors, and can effectively improve the recognition rate of images.\",\"PeriodicalId\":286364,\"journal\":{\"name\":\"Conference on Computer Graphics, Artificial Intelligence, and Data Processing\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Conference on Computer Graphics, Artificial Intelligence, and Data Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2674863\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference on Computer Graphics, Artificial Intelligence, and Data Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2674863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
BP neural network can recognize the image intelligently
Aiming at the disadvantages of slow convergence speed and unable to reduce recognition errors quickly, BP neural network is used for intelligent recognition of computer images. On this basis, the image recognition model is established by using BP neural network, and its modeling and modeling are carried out. Under the same experimental conditions, different recognition algorithms are compared. The results show that this algorithm has a better convergence speed, can effectively reduce errors, and can effectively improve the recognition rate of images.