{"title":"基于神经网络算法的AOI系统速度控制","authors":"Chun-Jung Chen, L. Shiau, Tien-Chi Chen","doi":"10.1109/TAAI.2016.7880155","DOIUrl":null,"url":null,"abstract":"This paper presents a two layer recurrent neural network employed in glass speed control transmitted by linear servo motor in Automated Optical Inspection (AOI) system platform. The recurrent neural network consists of an identifier and a controller, the identifier is used to catch a feedback signal from the position sensor and the controller is processed in microprocessor in order to supply an adaptive PWM signal. The glass in AOI is transmitted and controlled by linear servo motor. The PWM was processed by dsPIC30F30XX series microprocessor. The performance of the proposed method was demonstrated very good performance. The theoretic formulations of the proposed neural networks were derived. The stability of the proposed method was also analyzed and demonstrated.","PeriodicalId":159858,"journal":{"name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Speed control in AOI system by using neural networks algorithm\",\"authors\":\"Chun-Jung Chen, L. Shiau, Tien-Chi Chen\",\"doi\":\"10.1109/TAAI.2016.7880155\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a two layer recurrent neural network employed in glass speed control transmitted by linear servo motor in Automated Optical Inspection (AOI) system platform. The recurrent neural network consists of an identifier and a controller, the identifier is used to catch a feedback signal from the position sensor and the controller is processed in microprocessor in order to supply an adaptive PWM signal. The glass in AOI is transmitted and controlled by linear servo motor. The PWM was processed by dsPIC30F30XX series microprocessor. The performance of the proposed method was demonstrated very good performance. The theoretic formulations of the proposed neural networks were derived. The stability of the proposed method was also analyzed and demonstrated.\",\"PeriodicalId\":159858,\"journal\":{\"name\":\"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)\",\"volume\":\"57 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TAAI.2016.7880155\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAAI.2016.7880155","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Speed control in AOI system by using neural networks algorithm
This paper presents a two layer recurrent neural network employed in glass speed control transmitted by linear servo motor in Automated Optical Inspection (AOI) system platform. The recurrent neural network consists of an identifier and a controller, the identifier is used to catch a feedback signal from the position sensor and the controller is processed in microprocessor in order to supply an adaptive PWM signal. The glass in AOI is transmitted and controlled by linear servo motor. The PWM was processed by dsPIC30F30XX series microprocessor. The performance of the proposed method was demonstrated very good performance. The theoretic formulations of the proposed neural networks were derived. The stability of the proposed method was also analyzed and demonstrated.