{"title":"基于自反馈模板权值的比例记忆细胞神经网络(RMCNN)的模式学习与识别设计","authors":"Chiu-Hung Cheng, Chung-Yu Wu","doi":"10.1109/CNNA.2002.1035102","DOIUrl":null,"url":null,"abstract":"In this paper, a new type of the ratio-memory cellular neural network (RMCNN) with spatial-dependent self-feedback A-template weights is proposed and designed to recognize and classify the black-white image patterns. In the proposed RMCNN, the combined four-quadrant multiplier and two-quadrant divider with separated magnitude and sign is used to implement the Hebbian learning function and the ratio memory. To enhance the capability of pattern learning and recognition from noisy input patterns, the Z-template and the spatial-dependent self-feedback weights in the template A are applied to the proposed new type of RMCNN. The pattern learning and recognition function of the 18/spl times/18 RMCNN is simulated by Matlab software. It has been verified that the advanced RMCNN has the advantages of more stored patterns for recognition, and better recovery rate as compared to the original RMCNN. Thus the proposed RMCNN has great potential in the applications of neural associate memory for image processing.","PeriodicalId":387716,"journal":{"name":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2002-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"The design of ratio-memory cellular neural network (RMCNN) with self-feedback template weight for pattern learning and recognition\",\"authors\":\"Chiu-Hung Cheng, Chung-Yu Wu\",\"doi\":\"10.1109/CNNA.2002.1035102\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a new type of the ratio-memory cellular neural network (RMCNN) with spatial-dependent self-feedback A-template weights is proposed and designed to recognize and classify the black-white image patterns. In the proposed RMCNN, the combined four-quadrant multiplier and two-quadrant divider with separated magnitude and sign is used to implement the Hebbian learning function and the ratio memory. To enhance the capability of pattern learning and recognition from noisy input patterns, the Z-template and the spatial-dependent self-feedback weights in the template A are applied to the proposed new type of RMCNN. The pattern learning and recognition function of the 18/spl times/18 RMCNN is simulated by Matlab software. It has been verified that the advanced RMCNN has the advantages of more stored patterns for recognition, and better recovery rate as compared to the original RMCNN. Thus the proposed RMCNN has great potential in the applications of neural associate memory for image processing.\",\"PeriodicalId\":387716,\"journal\":{\"name\":\"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CNNA.2002.1035102\",\"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 of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.2002.1035102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The design of ratio-memory cellular neural network (RMCNN) with self-feedback template weight for pattern learning and recognition
In this paper, a new type of the ratio-memory cellular neural network (RMCNN) with spatial-dependent self-feedback A-template weights is proposed and designed to recognize and classify the black-white image patterns. In the proposed RMCNN, the combined four-quadrant multiplier and two-quadrant divider with separated magnitude and sign is used to implement the Hebbian learning function and the ratio memory. To enhance the capability of pattern learning and recognition from noisy input patterns, the Z-template and the spatial-dependent self-feedback weights in the template A are applied to the proposed new type of RMCNN. The pattern learning and recognition function of the 18/spl times/18 RMCNN is simulated by Matlab software. It has been verified that the advanced RMCNN has the advantages of more stored patterns for recognition, and better recovery rate as compared to the original RMCNN. Thus the proposed RMCNN has great potential in the applications of neural associate memory for image processing.