{"title":"基于CMOS技术的最小/最大细胞神经网络(MMCNNS)设计","authors":"Wen-Cheng Yen, Rongna Chen, Jui-Lin Lai","doi":"10.1109/CNNA.2002.1035068","DOIUrl":null,"url":null,"abstract":"The first VLSI implementation of the fuzzy cellular neural network (FCNN) structure is presented. The MIN/MAX CNN (MMCNN) is a special case of type-II FCNN, which consists only of local MIN and MAX operations. Due to the simple structure of the MMCNN, it is very suitable for VLSI implementation in image processing. Only one neuron cell, two multipliers, and nine min/max circuits realize the proposed MMCNN. Correct functions of the MMCNN in the erosion and dilation of the gray-scale mathematical morphology operation have been successfully verified in HSPICE simulation. FCNNs have great potential in the VLSI implementation of neural network systems in various signal processing applications.","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":"2","resultStr":"{\"title\":\"Design of MIN/MAX cellular neural networks (MMCNNS) in CMOS technology\",\"authors\":\"Wen-Cheng Yen, Rongna Chen, Jui-Lin Lai\",\"doi\":\"10.1109/CNNA.2002.1035068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The first VLSI implementation of the fuzzy cellular neural network (FCNN) structure is presented. The MIN/MAX CNN (MMCNN) is a special case of type-II FCNN, which consists only of local MIN and MAX operations. Due to the simple structure of the MMCNN, it is very suitable for VLSI implementation in image processing. Only one neuron cell, two multipliers, and nine min/max circuits realize the proposed MMCNN. Correct functions of the MMCNN in the erosion and dilation of the gray-scale mathematical morphology operation have been successfully verified in HSPICE simulation. FCNNs have great potential in the VLSI implementation of neural network systems in various signal processing applications.\",\"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\":\"2\",\"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.2002.1035068\",\"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.2002.1035068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design of MIN/MAX cellular neural networks (MMCNNS) in CMOS technology
The first VLSI implementation of the fuzzy cellular neural network (FCNN) structure is presented. The MIN/MAX CNN (MMCNN) is a special case of type-II FCNN, which consists only of local MIN and MAX operations. Due to the simple structure of the MMCNN, it is very suitable for VLSI implementation in image processing. Only one neuron cell, two multipliers, and nine min/max circuits realize the proposed MMCNN. Correct functions of the MMCNN in the erosion and dilation of the gray-scale mathematical morphology operation have been successfully verified in HSPICE simulation. FCNNs have great potential in the VLSI implementation of neural network systems in various signal processing applications.