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Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)最新文献

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Circulant matrices and the stability theory of CNNs 循环矩阵与cnn的稳定性理论
Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94) Pub Date : 1994-12-18 DOI: 10.1109/CNNA.1994.381685
M. Joy, V. Tavsanoglu
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引用次数: 1
Transient response computation of a mechanical vibrating system using cellular neural networks 基于细胞神经网络的机械振动系统瞬态响应计算
Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94) Pub Date : 1994-12-18 DOI: 10.1109/CNNA.1994.381659
P. Szolgay, G. Voros
{"title":"Transient response computation of a mechanical vibrating system using cellular neural networks","authors":"P. Szolgay, G. Voros","doi":"10.1109/CNNA.1994.381659","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381659","url":null,"abstract":"Cellular neural networks (CNNs) paradigm is applied in the paper to compute the transient response of mechanical vibrating systems. Based on previous theoretical results on this field we would like to show (i) how the CNN templates can be generated automatically by a subroutine from the COSMOS/M finite element analysis system; (ii) how we assign to each degree of freedom two coupled CNN layers and how the templates are derived. Some interesting examples are shown and analyzed.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123777774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
An experimental system for path tracking of a robot using a 16*16 connected component detector CNN chip with direct optical input 采用直接光输入的16*16连接组件检测器CNN芯片进行机器人路径跟踪的实验系统
Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94) Pub Date : 1994-12-18 DOI: 10.1109/CNNA.1994.381669
P. Szolgay, A. Katona, G. Eross, A. Kiss
{"title":"An experimental system for path tracking of a robot using a 16*16 connected component detector CNN chip with direct optical input","authors":"P. Szolgay, A. Katona, G. Eross, A. Kiss","doi":"10.1109/CNNA.1994.381669","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381669","url":null,"abstract":"An experimental system was built up and tested for optical path tracking of a robot, where a 16*16 connected component detector chip with direct optical input was used. The speed of computation in the experimental architecture was analyzed.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131949330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Optimal solutions of selected cellular neural network applications by the hardware annealing method 用硬件退火法对选定的细胞神经网络应用进行最优解
Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94) Pub Date : 1994-12-18 DOI: 10.1109/CNNA.1994.381666
B. Sheu, S. Bang, W. Fang
{"title":"Optimal solutions of selected cellular neural network applications by the hardware annealing method","authors":"B. Sheu, S. Bang, W. Fang","doi":"10.1109/CNNA.1994.381666","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381666","url":null,"abstract":"An engineering annealing method for optimal solutions of cellular neural networks is presented. Cellular neural networks have great potential in solving many important scientific problems in signal processing and optimization by the use of predetermined templates. Hardware annealing (SaHyun Bang, 1994), which is a paralleled version of effective mean field annealing in analog networks, is a highly efficient method of finding optimal solutions for cellular neural networks. It does not require any stochastic procedure and henceforth can be very fast. The generalized energy function of the network is first increased by reducing the voltage gain of each neuron. Then, the hardware annealing searches for the globally minimum energy state by continuously increasing the gain of neurons. The process of global optimization by the proposed hardware annealing method can be described by eigenvalues in the time varying dynamic system.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127489365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
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