IEEE International Workshop on Cellular Neural Networks and their Applications最新文献_第3页

Finding multiple equilibrium points of cellular neural networks without enumeration 不枚举求细胞神经网络的多个平衡点

IEEE International Workshop on Cellular Neural Networks and their Applications Pub Date : 1990-12-16 DOI: 10.1109/CNNA.1990.207506

L. Vandenberghe, J. Vandewalle

引用次数: 9

A cellular neural network embedded in a dual computing structure (CNND) and its use for character recognition 一种嵌入二元计算结构(CNND)的细胞神经网络及其在字符识别中的应用

IEEE International Workshop on Cellular Neural Networks and their Applications Pub Date : 1990-12-16 DOI: 10.1109/CNNA.1990.207511

T. Szirányi, T. Roska, P. Szolgay

{"title":"A cellular neural network embedded in a dual computing structure (CNND) and its use for character recognition","authors":"T. Szirányi, T. Roska, P. Szolgay","doi":"10.1109/CNNA.1990.207511","DOIUrl":"https://doi.org/10.1109/CNNA.1990.207511","url":null,"abstract":"Analog neural networks and various regular analog processing arrays are superior in speed, in various tasks, to digital-logical machines. However, in real problems logical decisions and control are generally inevitable. The combination of analog neural and digital logical systems in a dual computing structure has been proposed by T. Roska (1987). A special type of dual computing structure is proposed as a system for character or symbol recognition purposes. It contains a cellular neural network for real-time feature extraction. This cellular neural network embedded in a dual computing structure (CNND) provides a framework for designing algorithms containing sequences of analog and digital operations. This CNND architecture is then used for some well-defined character recognition tasks. Mainly the recognition of printed characters is studied. Several dual algorithms are introduced for classifying various classes of printed character sets. These algorithms contain analog CNN cloning templates and digital decision functions. Preliminary experimental results are presented.<<ETX>>","PeriodicalId":142909,"journal":{"name":"IEEE International Workshop on Cellular Neural Networks and their Applications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134576160","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: 6

ASIM, an efficient simulation environment for cellular neural networks and analog arrays 一个有效的模拟环境，用于细胞神经网络和模拟阵列

IEEE International Workshop on Cellular Neural Networks and their Applications Pub Date : 1990-12-16 DOI: 10.1109/CNNA.1990.207518

K. R. Krieg, L. Chua

{"title":"ASIM, an efficient simulation environment for cellular neural networks and analog arrays","authors":"K. R. Krieg, L. Chua","doi":"10.1109/CNNA.1990.207518","DOIUrl":"https://doi.org/10.1109/CNNA.1990.207518","url":null,"abstract":"Summary form only given. Traditional circuit-based simulation of cellular neural networks and analog arrays is very slow and cumbersome. Much of the computation and memory space involved could be reduced if the data and computational structures of the program could mirror the architecture of the analog array. The authors present the details of such a simulator, named ASIM. The computations performed by ASIM are derived directly from the nonlinear differential equations describing the processing at each node and the internal data representation mirrors the architecture of analog arrays. ASIM is a graphical environment for simulating continuous-time continuous-variable computational arrays which have a regular structure, whose connectivity is nearest-neighbor, and whose nodal processing can be described by a set of nonlinear differential equations. The user need specify only the equations for nodal processing and the connectivity to neighboring processors. Input and output are graphical and show the state of all variables in the array as a function of time. The user may incorporate iteration of any internal variable to find optimal processing strategies or neighborhood organization. To aid the evaluation of algorithms which are to be fabricated in VLSI, the use can specify variances in any computational variable to simulate the effect of fabrication tolerances and may specify that certain variables have associated noise components. Both of these features make ASIM an ideal aid in developing analog array and cellular neural network algorithms which are more robust to implementation variability. The ASIM program runs on 80386 based IBM PC/AT computers using the MS-DOS operating system. It requires 2 Mbytes of extended memory and a VGA compatible graphics card.<<ETX>>","PeriodicalId":142909,"journal":{"name":"IEEE International Workshop on Cellular Neural Networks and their Applications","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123846079","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: 1

Analog VLSI implementation of a neural network with competitive learning 模拟VLSI实现的一个具有竞争性学习的神经网络

IEEE International Workshop on Cellular Neural Networks and their Applications Pub Date : 1990-12-16 DOI: 10.1109/CNNA.1990.207525

F. Pelayo, A. Prieto, B. Pino, P. Martín-Smith

引用次数: 9

The non-idealities of the IC-realization and the stability of CNN-networks 集成电路实现的非理想性与cnn网络的稳定性

IEEE International Workshop on Cellular Neural Networks and their Applications Pub Date : 1990-12-16 DOI: 10.1109/CNNA.1990.207537

K. Halonen, J. Vaananen

引用次数: 9

The VLSI kernel of neural algorithms VLSI内核神经算法

IEEE International Workshop on Cellular Neural Networks and their Applications Pub Date : 1990-12-16 DOI: 10.1109/CNNA.1990.207524

U. Ramacher

引用次数: 4

Detecting simple motion using cellular neural networks 用细胞神经网络检测简单运动

IEEE International Workshop on Cellular Neural Networks and their Applications Pub Date : 1990-12-16 DOI: 10.1109/CNNA.1990.207516

T. Roska, T. Boros, Patrick Thiran, L. Chua

引用次数: 116

Cellular neural networks with nonlinear and delay-type template elements 具有非线性和延迟型模板元素的细胞神经网络

IEEE International Workshop on Cellular Neural Networks and their Applications Pub Date : 1990-12-16 DOI: 10.1109/CNNA.1990.207503

T. Roska, L. Chua

引用次数: 214

Realization of CNNs by optical parallel processing with spatial light valves 空间光阀光学并行处理实现cnn

IEEE International Workshop on Cellular Neural Networks and their Applications Pub Date : 1990-12-16 DOI: 10.1109/CNNA.1990.207533

N. Fruhauf, E. Luder

引用次数: 16

A hardware accelerator board for cellular neural networks: CNN-HAC 用于细胞神经网络的硬件加速板:CNN-HAC

IEEE International Workshop on Cellular Neural Networks and their Applications Pub Date : 1990-12-16 DOI: 10.1109/CNNA.1990.207520

T. Roska, G. Bártfai, P. Szolgay, T. Szirányi, A. Radványi, T. Kozek, Z. Ugray

引用次数: 24