SC-CNN BASED MULTIFUNCTION SIGNAL GENERATOR

2007 ◽  
Vol 17 (12) ◽  
pp. 4387-4393 ◽  
Author(s):  
RECAI KILIÇ
Keyword(s):  
Neural Network ◽  
Computer Simulations ◽  
Laboratory Experiments ◽  
Cellular Neural Network ◽  
Signal Generator ◽  
Signal Generation ◽  
Engineering Applications ◽  
Chua's Circuit ◽  
Future Studies ◽  
Design Idea

This paper presents a very versatile multifunction signal generator tool. The proposed generator is based on State Controlled Cellular Neural Network (SC-CNN) based Chua's circuit and it has two signal generation modes, namely CM (Chaos Mode) and FM (Function Mode). While the generator is able to produce nonlinear chaotic waveforms in Chaos Mode, it is also able to generate other classical sinusoidal, triangle and square waveforms in Function Mode. The proposed design idea has been validated through computer simulations and laboratory experiments. Future studies with the proposed generator tool will contribute to further developments in SC-CNN based engineering applications.

scholarly journals REALIZATION AND BIFURCATION OF BOOLEAN FUNCTIONS VIA CELLULAR NEURAL NETWORKS

2005 ◽  
Vol 15 (07) ◽  
pp. 2109-2129 ◽  
Author(s):  
FANGYUE CHEN ◽  
GUANRONG CHEN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Boolean Function ◽  
Boolean Functions ◽  
Cellular Neural Network ◽  
Cellular Neural Networks ◽  
Gene Bank ◽  
Engineering Applications ◽  
Binary Input

In this work, we study the realization and bifurcation of Boolean functions of four variables via a Cellular Neural Network (CNN). We characterize the basic relations between the genes and the offsets of an uncoupled CNN as well as the basis of the binary input vectors set. Based on the analysis, we have rigorously proved that there are exactly 1882 linearly separable Boolean functions of four variables, and found an effective method for realizing all linearly separable Boolean functions via an uncoupled CNN. Consequently, any kind of linearly separable Boolean function can be implemented by an uncoupled CNN, and all CNN genes that are associated with these Boolean functions, called the CNN gene bank of four variables, can be easily determined. Through this work, we will show that the standard CNN invented by Chua and Yang in 1988 indeed is very essential not only in terms of engineering applications but also in the sense of fundamental mathematics.

AN EXPERIMENTAL STUDY ON CHAOTIC DYNAMICS OF CFOA-BASED SC-CNN CIRCUIT

2005 ◽  
Vol 15 (08) ◽  
pp. 2551-2558 ◽  
Author(s):  
ENIS GÜNAY ◽  
MUSTAFA ALÇI ◽  
FATMA YILDIRIM
Keyword(s):  
Neural Network ◽  
High Frequency ◽  
Chaotic Dynamics ◽  
Laboratory Experiments ◽  
Cellular Neural Network ◽  
Current Feedback ◽  
Op Amp ◽  
Op Amps ◽  
Experimental Implementation ◽  
High Frequency Performance

In this paper, an experimental implementation of State Controlled Cellular Neural Network (SC-CNN) circuit using Current Feedback Op Amp (CFOA) is presented and its chaotic dynamics including high frequency performance are investigated by laboratory experiments. Depending on its significant advantages over the conventional voltage op amps (VOAs), without imposing any restrictions, the CFOAs have been used instead of the VOAs in SC-CNN circuit. Experimental results have shown that the proposed implementation has a capacity of higher frequency operation.

Observation of Strange Nonchaotic Dynamics in the Frame of State-Controlled Cellular Neural Network-Based Oscillator

2019 ◽  
Vol 14 (11) ◽  
Author(s):  
P. Megavarna Ezhilarasu ◽  
K. Suresh ◽  
K. Thamilmaran
Keyword(s):  
Neural Network ◽  
Lyapunov Exponents ◽  
Laboratory Experiments ◽  
Cellular Neural Network ◽  
Phase Sensitivity ◽  
Chaotic Circuit ◽  
Finite Time Lyapunov Exponents ◽  
Periodically Driven ◽  
Largest Lyapunov Exponents ◽  
Strange Nonchaotic Attractors

Abstract In this paper, the strange nonchaotic dynamics of a quasi-periodically driven state-controlled cellular neural network (SC-CNN) based on a simple chaotic circuit is investigated using hardware experiments and numerical simulations. We report here two different routes to strange nonchaotic attractors (SNAs) taken by this SC-CNN based circuit system. These routes were confirmed using rational approximation (RA) theory, finite time Lyapunov exponents, spectrum of the largest Lyapunov exponents and their variance, and phase sensitivity exponent. It is observed that the results from both computer simulations as well as laboratory experiments have spectacular resemblance.

CHAOTIC CELLULAR NEURAL NETWORKS MADE OF CHUA'S CIRCUITS

1993 ◽  
Vol 03 (02) ◽  
pp. 603-612
Author(s):  
C. GÜZELIŞ
Keyword(s):  
Neural Network ◽  
Information Processing ◽  
Cellular Neural Network ◽  
Chua’S Circuit ◽  
Nonlinear Signal Processing ◽  
Chua's Circuit ◽  
Complex Information ◽  
Complex Information Processing ◽  
Nonlinear Signal

A chaotic neural network, called chaotic Cellular Neural Network (CNN), is proposed for performing complex information processing tasks. Each cell in the chaotic CNN is a Chua's circuit and connected only to its nearest neighbors. The proposed network of coupled Chua's circuit type cells constitutes a special case of the generalized CNNs introduced recently.1 Individual cells play the role of an analog microprocessor: producing constant, oscillatory or chaotic steady-state outputs depending on its input, which is the weighted sum of external inputs and the outputs of neighboring cells. The proposed chaotic CNN has complex temporal dynamical behaviours and hence provides a potentially rich mechanism for information processing, specially for nonlinear signal processing.

Optimizing Time-multiplexing Raster Cellular Neural Network simulator using genetic algorithms with RK8(6)

2011 ◽  
Vol 3 (6) ◽  
pp. 87-90
Author(s):  
O. H. Abdelwahed O. H. Abdelwahed ◽  
◽  
M. El-Sayed Wahed ◽  
O. Mohamed Eldaken
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Cellular Neural Network ◽  
Network Simulator

Cellular Neural Network Based on Hybrid Linear Matrix Inequality and Particle Swarm Optimization for Noise Removal of Color Image

2011 ◽  
Vol 422 ◽  
pp. 771-774
Author(s):  
Te Jen Su ◽  
Jui Chuan Cheng ◽  
Yu Jen Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Linear Matrix Inequality ◽  
Color Image ◽  
Particle Swarm ◽  
Cellular Neural Network ◽  
Noise Removal ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Swarm Optimization

This paper presents a color image noise removal technique that employs a cellular neural network (CNN) based on hybrid linear matrix inequality (LMI) and particle swarm optimization (PSO). For designing templates of CNN, the Lyapunov stability theorem is applied to derive the criterion for the uniqueness and global asymptotic stability of the CNN’s equilibrium point. The template design is characterized as a standard LMI problem, and the parameters of templates are optimized by PSO. The input templates are obtained by employing the CNN’s property of saturation nonlinearity, which can be used to eliminate noise from arbitrary corrupted images. The demonstrated examples are compared favorably with other available methods, which illustrate the better performance of the proposed LMI-PSO-CNN methodology.

Sigma–delta cellular neural network for 2D modulation

2008 ◽  
Vol 21 (2-3) ◽  
pp. 349-357 ◽  
Author(s):  
Hisashi Aomori ◽  
Tsuyoshi Otake ◽  
Nobuaki Takahashi ◽  
Mamoru Tanaka
Keyword(s):  
Neural Network ◽  
Cellular Neural Network ◽  
Sigma Delta
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