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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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Cellular neural network for mobile robot navigation 移动机器人导航的细胞神经网络
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.1994.381665
B. Siemiątkowska
{"title":"Cellular neural network for mobile robot navigation","authors":"B. Siemiątkowska","doi":"10.1109/CNNA.1994.381665","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381665","url":null,"abstract":"In this paper an application of cellular neural network for a mobile robot navigation is presented. The following problem is addressed: knowing the goal position and the robot position find a continuous and collision free path leading from the goal to the robot. The map of the environment is unknown. Two layer cellular neural network is used for path planning. Experimental results show feasibility of the proposed method and some practical advantages in comparison to the classical approaches.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"83 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":"116319589","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: 17
A CNN universal chip in CMOS technology 采用CMOS技术的CNN通用芯片
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.1994.381701
S. Espejo, R. Carmona-Galán, R. Domínguez-Castro, Á. Rodríguez-Vázquez
{"title":"A CNN universal chip in CMOS technology","authors":"S. Espejo, R. Carmona-Galán, R. Domínguez-Castro, Á. Rodríguez-Vázquez","doi":"10.1109/CNNA.1994.381701","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381701","url":null,"abstract":"This paper describes the design of a CNN universal chip in a standard CMOS technology. The core of the chip consists of an array of 32/spl times/32 completely programmable CNN cells. Input image can be loaded in optical or electrical form. Accuracy is in the range of 7-8 bit and cell density is of 33 cells/mm/sup 2/.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"396 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":"124748144","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: 163
Robust oscillations and bifurcations in cellular neural networks 细胞神经网络的鲁棒振荡和分岔
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.1994.381662
R. Dogaru, A.T. Murgan, D. Ioan
{"title":"Robust oscillations and bifurcations in cellular neural networks","authors":"R. Dogaru, A.T. Murgan, D. Ioan","doi":"10.1109/CNNA.1994.381662","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381662","url":null,"abstract":"A software package for studying discrete time neural networks dynamics is presented as an efficient analysis tool having also capabilities for designing special purpose cellular neural networks (CNN's) such as period controlled oscillators, noise generators and chaotic based systems. Based on an information theory approach, an entropy associated with a given structure of the network was defined as a global descriptor of the system dynamics. Any kind of discrete time neural model with any size is allowed, including CNN's as a particular case. By performing two or one dimensional analysis trough the weights space, some new properties of the opposite-template CNN's were discovered, e.g. the existence of robustness domains which means that small controllable change in weights values implies no change in the network's entropy. Using this software package, robust chaotic networks capable of generating white-noise like signals were also discovered.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"48 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":"130685511","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: 7
A CMOS current-mode VLSI implementation of cellular neural network for an image objects area estimation 一种CMOS电流模VLSI的细胞神经网络实现，用于图像对象的面积估计
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.1994.381652
J. Kowalski, K. Slot, T. Kacprzak
{"title":"A CMOS current-mode VLSI implementation of cellular neural network for an image objects area estimation","authors":"J. Kowalski, K. Slot, T. Kacprzak","doi":"10.1109/CNNA.1994.381652","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381652","url":null,"abstract":"Summary form only given. The paper is concerned with the physical implementation of a CNN which realizes a specific predefined image processing operation. The circuit is intended to be realized in a 1.2 /spl mu/m CMOS process. The VLSI chip is expected to be used as a smart sensor of visual information and its main function is to detect objects with an area which exceeds some user-defined threshold value. For proper operation the circuit should provide an appropriate image preprocessing to cancel the effects of non-uniform image illumination and to suppress image noise. The kernel of the proposed circuit is a reprogrammable CNN, based on the concept of a pulse-mode CNN. The paper presents an architecture of a basic element of the circuit-a reprogrammable cell, designed using the current-mode approach. The proposed cell architecture differs from the solutions presented so far since it is optimized to ensure these processing properties, which are important for proper circuit operation.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"50 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":"123094500","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: 2
Use of the CNN dynamic to associate two points with different quantization grains in the state space 利用CNN动态将两个点与状态空间中不同的量化颗粒相关联
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.1994.381637
M. Coli, P. Palazzari, R. Rughi
{"title":"Use of the CNN dynamic to associate two points with different quantization grains in the state space","authors":"M. Coli, P. Palazzari, R. Rughi","doi":"10.1109/CNNA.1994.381637","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381637","url":null,"abstract":"The paper is concerned with the design of a part of the CNN state space trajectory. A point in the CNN state space represents a sampled signal (the state of each neuron is a sample): the set of points generated by the CNN state evolution can thus represent a set of sampled signals. We describe a methodology which allows us to find the initial state and the CNN weights so that the CNN state evolution is, at a fixed time t/sub 0/, as close as possible to the point representing a given sampled signal. In such way a signal is described through the CNN initial state, the cloning template and the time instant t/sub 0/. In order to find the CNN initial state and the CNN weights we used a procedure based on Genetic Algorithms.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"181 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":"122286494","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: 0
Global learning algorithms for discrete-time cellular neural networks 离散时间细胞神经网络的全局学习算法
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.1994.381690
H. Magnussen, J. Nossek
{"title":"Global learning algorithms for discrete-time cellular neural networks","authors":"H. Magnussen, J. Nossek","doi":"10.1109/CNNA.1994.381690","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381690","url":null,"abstract":"Two learning algorithms for discrete-time cellular neural networks (DTCNNs) are proposed, which do not require the a priori knowledge of the output trajectory of the network. A cost function is defined, which is minimized by direct search optimization methods and simulated annealing.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"18 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":"116553191","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: 14
Analog VLSI implementation of a nonlinear systems model of the hippocampal brain region 模拟VLSI实现的一个海马体脑区非线性系统模型
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.1994.381708
T. Berger, B. Sheu, R. Tsai
{"title":"Analog VLSI implementation of a nonlinear systems model of the hippocampal brain region","authors":"T. Berger, B. Sheu, R. Tsai","doi":"10.1109/CNNA.1994.381708","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381708","url":null,"abstract":"The hippocampus is a major brain system involved in learning and memory functions, and consists of multiple populations of neurons with strongly nonlinear properties that are interconnected both locally and non-locally. An analog VLSI design has been developed that allows different classes of nonlinearities specific to each neuron population to define the transfer function of a network of neurons implemented in hardware. Principles of a CNN design have been used to generate local interactions between adjacent processing elements. Non-local interactions will be implemented in future designs with the use of multiple chips. In this manner, we are attempting to better integrate into a hardware device the unique information processing and learning capabilities of real biological neurons known to perform those functions.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"34 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":"132692042","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: 3
A novel approach to the convergence of neural networks for signal processing 信号处理中神经网络收敛的一种新方法
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.1994.381627
Ruey-Wen Liu, Yih-Fang Huang, X. Ling
{"title":"A novel approach to the convergence of neural networks for signal processing","authors":"Ruey-Wen Liu, Yih-Fang Huang, X. Ling","doi":"10.1109/CNNA.1994.381627","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381627","url":null,"abstract":"Summary form only given. A novel deterministic approach to the convergence of (stochastic) learning algorithms is presented. The link is the new concept of time-average invariance which is a property of deterministic signals but resembles the realizations of stochastic signals that are ergodic and stationary. An unsupervised learning algorithm is considered. Signals are viewed as deterministic functions, but satisfy a property called time-average invariance. As such, deterministic-based analysis can be applied to stochastic-like signals. Consequently, the complexity of the convergence analysis is significantly reduced.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"60 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":"133919230","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: 12
Using CNN to unravel space-time processing in the vertebrate retina 利用CNN揭示脊椎动物视网膜的时空处理
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.1994.381683
F. Werblin, A. Jacobs
{"title":"Using CNN to unravel space-time processing in the vertebrate retina","authors":"F. Werblin, A. Jacobs","doi":"10.1109/CNNA.1994.381683","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381683","url":null,"abstract":"The vertebrate retina performs a number of highly complex transformations in space and time. Because we have not had adequate tools for analyzing these functions, little is presently known about the mechanisms underlying these transformations. CNN provides, for the first time, an analytical framework within which these transformations can be predicted, measured and analyzed. While conventional analyses have relied on studies of only single cells CNN allows us to think about, manipulate generate and study patterns of activity involving large populations of cells. Thus, CNN promises to unravel some of the important mechanisms by which the retina abstracts and encodes the visual message in space and time.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"41 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":"115558396","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: 23
A versatile CMOS building block for fully analogically-programmable VLSI cellular neural networks 用于完全模拟可编程VLSI细胞神经网络的通用CMOS构建块
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.1994.381654
A. Piovaccari, G. Setti
{"title":"A versatile CMOS building block for fully analogically-programmable VLSI cellular neural networks","authors":"A. Piovaccari, G. Setti","doi":"10.1109/CNNA.1994.381654","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381654","url":null,"abstract":"The design of a new CMOS building block to be used for analogically programming the control and the feedback operators of cellular neural networks is reported. The circuit was used for a repetitive programming procedure for motion detection in a 9000 transistors 7/spl times/7 CNN.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"8 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":"114183049","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
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