Results 241 to 250 of about 4,958,442 (285)

AUTOWAVES IN MEMRISTIVE CELLULAR NEURAL NETWORKS

International Journal of Bifurcation and Chaos, 2012
Memristor has received a significant amount of attention after a solid state implementation of it was realized in the Hewlett–Packard laboratories, because of the potential applications of such device in different areas. In this brief paper, we introduce a Cellular Neural/Nonlinear Network based on memristive cells for autowave generation.
V. T. Pham, A. Buscarino, FORTUNA, Luigi, FRASCA, MATTIA   +3 more
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CELLULAR NEURAL NETWORKS FOR EDGE DETECTION

International Journal of Bifurcation and Chaos, 2007
This Letter presents an effective edge detection technique based on the cellular neural network paradigm. The approach exploits a rigorous model of the image contours and takes into account some electrical restrictions of existing hardware implementations.
Grassi, G., Vecchio, P., DI SCIASCIO, Eugenio, GRIECO, Luigi Alfredo   +3 more
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AUTOASSOCIATIVE MEMORY CELLULAR NEURAL NETWORKS

International Journal of Bifurcation and Chaos, 2010
An autoassociative memory is a device which accepts an input pattern and generates an output as the stored pattern which is most closely associated with the input. In this paper, we propose an autoassociative memory cellular neural network, which consists of one-dimensional cells with spatial derivative inputs, thresholds and memories.
Makoto Itoh, Leon O. Chua
openaire   +1 more source

HORSESHOE CHAOS IN CELLULAR NEURAL NETWORKS

International Journal of Bifurcation and Chaos, 2006
In this paper, we demonstrate chaos in low dimensional cellular neural networks for some weight matrices. To verify chaoticity of the dynamics in these cellular neural networks, we consider a cross-section properly chosen for the attractors obtained and study the dynamics of the corresponding Poincaré maps, and rigorously verify the existence of ...
Xiao-Song Yang, Qingdu Li
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BIFURCATIONS AND CHAOS IN CELLULAR NEURAL NETWORKS

Journal of Circuits, Systems and Computers, 2003
The dynamic behavior of first-order autonomous space invariant cellular neural networks (CNNs) is investigated. It is shown that complex dynamics may occur in very simple CNN structures, described by two-dimensional templates that present only vertical and horizontal couplings.
BIEY, MARIO, CHECCO P, GILLI, MARCO
openaire   +3 more sources

Discrete‐time cellular neural networks

International Journal of Circuit Theory and Applications, 1992
AbstractA network structure called a discrete‐time cellular neural network is introduced. It is derived from cellular neural networks and feedback threshold networks. the architecture is discussed and its advantages are presented. Convergence is proved for a large class of templates and applications are given for the following image‐processing tasks ...
Hubert Harrer, Josef A. Nossek
openaire   +3 more sources

Cellular neural networks to explore complexity

Soft Computing - A Fusion of Foundations, Methodologies and Applications, 1997
In this paper the fundamentals of Cellular Neural Networks (CNNs) are introduced. Subsequently it is shown that, due to their locally distributed way of exchanging signals, such structures can be used as powerful devices to simulate and to reproduce, in an analog fashion and low cost, complex behaviors, i.e.
ARENA, Paolo Pietro, CAPONETTO, Riccardo, FORTUNA, Luigi, G. Manganaro   +3 more
openaire   +4 more sources

Learning of cellular neural networks

Future Generation Computer Systems, 2001
Summary: An approach to first-order Cellular Neural Networks (CNN) learning is suggested. It is based on the ideas of perceptron learning rule. The proposed method allows to find parameters of a CNN connection template which provides the formation of patterns with preset properties. The method was applied to learn 1D and 2D CNN with symmetric templates
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SMALE HORSESHOE OF CELLULAR NEURAL NETWORKS

International Journal of Bifurcation and Chaos, 2000
The paper shows the spatial disorder of one-dimensional Cellular Neural Networks (CNN) using the iteration map method. Under certain parameters, the map is two-dimensional and the Smale horseshoe is constructed. Moreover, we also illustrate the variant of CNN, closely related to Henón-type and Belykh maps, and discrete Allen–Cahn equations.
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On binary ouput of cellular neural networks

International Journal of Circuit Theory and Applications, 1997
Summary: This letter presents an amended proof of an important result, the binary output property (BOP), in the theory of cellular neural networks (CNNs). A CNN is a dynamical system of the form \[ \dot{\mathbf x}= -{\mathbf x}+ {\mathbf A}{\mathbf y}+{\mathbf k},\tag{1} \] where \({\mathbf x}\) is the state vector, \({\mathbf y}= f({\mathbf x})\) is ...
openaire   +3 more sources