Cellular neural networks: A review
V Cimagalli, M Balsi, E Caianiello - … Nets WIRN Vietri'93: Proc. of …, 1993 - World Scientific
ABSTRACT A unified review of the Cellular Neural Network paradigm is attempted. First of
all, general theoretical framework is stated, followed by description of particular models …
all, general theoretical framework is stated, followed by description of particular models …
Building cellular neural network templates with a hardware friendly learning algorithm
A general solution for the construction of Cellular Neural Network (CNN) weights (cloning
template) with Random Weight Change (RWC) algorithm is proposed. A target image for …
template) with Random Weight Change (RWC) algorithm is proposed. A target image for …
Recurrent perceptron learning algorithm for completely stable cellular neural networks
C Guzelis, S Karamahmut - Proceedings of the Third IEEE …, 1994 - ieeexplore.ieee.org
A supervised learning algorithm for obtaining the template coefficients in completely stable
cellular neural networks (CNNs) is presented. The proposed algorithm resembles the well …
cellular neural networks (CNNs) is presented. The proposed algorithm resembles the well …
SCNN: a universal simulator for cellular neural networks
R Kunz, R Tetzlaff, D Wolf - 1996 Fourth IEEE International …, 1996 - ieeexplore.ieee.org
In this paper a universal simulator for cellular neural network (CNN) is presented. CNN with
nonlinear and delay-type templates can be simulated precisely with SCNN, practically …
nonlinear and delay-type templates can be simulated precisely with SCNN, practically …
SCNN 2000. I. Basic structure and features of the simulation system for cellular neural networks
A Loncar, R Kunz, R Tetzlaff - Proceedings of the 2000 6th …, 2000 - ieeexplore.ieee.org
In this paper the basic structure and features of SCNN 2000, a universal simulation system
for cellular neural networks (CNN) is presented. Since the first presentation of SCNN (1996) …
for cellular neural networks (CNN) is presented. Since the first presentation of SCNN (1996) …
A recurrent perceptron learning algorithm for cellular neural networks
A supervised learning algorithm for obtaining the template coefficients in completely stable
Cellular Neural Networks (CNNs) is analysed in the paper. The considered algorithm …
Cellular Neural Networks (CNNs) is analysed in the paper. The considered algorithm …
Recurrent perceptron learning algorithm for CNNs with application to edge detection
C Guzelis, S Laramahmut - Proceedings of ICNN'95 …, 1995 - ieeexplore.ieee.org
A supervised learning algorithm for obtaining the template coefficients in completely stable
cellular neural networks (CNNs) is presented and it is applied for learning edge detection in …
cellular neural networks (CNNs) is presented and it is applied for learning edge detection in …
A learning algorithm for the dynamics of CNN with nonlinear templates. I. Discrete-time case
R Tetzlaff, D Wolf - … Workshop on Cellular Neural Networks and …, 1996 - ieeexplore.ieee.org
A learning algorithm for the dynamics of discrete-time cellular neural networks (DTCNN) with
gradient-based nonlinear templates is presented. For modeling the dynamics of nonlinear …
gradient-based nonlinear templates is presented. For modeling the dynamics of nonlinear …
The toroidal neural networks
M Coli, P Palazzari, R Rughi - 2000 IEEE International …, 2000 - ieeexplore.ieee.org
In this paper we present the toroidal neural networks (TNN), a new class of neural network
derived from discrete time-cellular neural networks (DT-CNN). TNN are characterized by 2D …
derived from discrete time-cellular neural networks (DT-CNN). TNN are characterized by 2D …
On cellular neural network learning
X Vilasís-Cardona… - Proceedings of the 2005 …, 2005 - ieeexplore.ieee.org
We study the continuous time two neuron CNN in the context of a supervised learning
problem and extract the convergence map for constant inputs. From this result we can …
problem and extract the convergence map for constant inputs. From this result we can …