An Analytical and Experimental Study of SC-CNN-Based Simple Nonautonomous Chaotic Circuit

2019 ◽  
Vol 14 (12) ◽  
Author(s):  
H. Shameem Banu ◽  
P. S. Sheik Uduman ◽  
K. Thamilmaran
Keyword(s):  
Neural Network ◽  
Circuit Simulation ◽  
Experimental Studies ◽  
Power Spectra ◽  
Cellular Neural Network ◽  
Phase Portraits ◽  
Explicit Analytical Solution ◽  
Periodic Dynamics ◽  
First Time ◽  
Good Agreement

Abstract In this study, we report an explicit analytical solution of state-controlled cellular neural network (SC-CNN) based second-order nonautonomous system. The proposed system is modeled with an aid of a generalized two-state-controlled cellular neural network (CNN) equations and experimentally realized by imposing a suitable connection of simple two-state-controlled generalized CNN cells following the report of Swathi et al. [2014]. The chaotic and quasi-periodic dynamics observed from this system have been investigated through an analytical approach for the first time. The intriguing dynamics observed from the system where further substantiated by phase portraits, Poincaré map, power spectra, and “0−1 test.” We trace the transition of the system from periodic to chaos through analytical solutions, which are in good agreement with hardware experiments. Additionally, we show PSpice circuit simulation results for validating our analytical and experimental studies.

Dynamics of SC-CNN Based Variant of MLC Circuit: An Experimental Study

2014 ◽  
Vol 24 (02) ◽  
pp. 1430008 ◽  
Author(s):  
P. S. Swathy ◽  
K. Thamilmaran
Keyword(s):  
Neural Network ◽  
Experimental Study ◽  
Time Series ◽  
Power Spectra ◽  
Cellular Neural Network ◽  
Phase Portraits ◽  
Onset Of Chaos ◽  
Experimental Time Series ◽  
The Stability ◽  
Good Agreement

In this paper, a State Controlled Cellular Neural Network (SC-CNN) based variant of Murali–Lakshmanan–Chua (MLCV) circuit is presented. The proposed system is modeled by using a suitable connection of two simple state controlled generalized CNN cells, while the stability of the circuit is studied by determining the eigenvalues of the stability matrices, the dynamics as well as onset of chaos, torus and bifurcation have been investigated through laboratory hardware experiments and numerical analysis of the generalized SC-CNN equations. The experimental results such as phase portraits, Poincaré map and power spectra are in good agreement with those of numerical computations. We further validate our findings with data obtained from both experimental time series observations and numerical simulations and discuss "0-1 test" for distinguishing quasiperiodicity and chaoticity, which successfully detects the transition. The results obtained are quite satisfactory and significant.

scholarly journals An Approach to Risk Assessment and Threat Prediction for Complex Object Security Based on a Predicative Self-Configuring Neural System

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 102
Author(s):  
Nikolai Vladimirovich Korneev ◽  
Julia Vasilievna Korneeva ◽  
Stasis Petrasovich Yurkevichyus ◽  
Gennady Ivanovich Bakhturin
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Fundamental Problem ◽  
Experimental Studies ◽  
Neural System ◽  
Assessment System ◽  
Classification Model ◽  
Complex Object ◽  
Assessment Problems ◽  
First Time

We identified a set of methods for solving risk assessment problems by forecasting an incident of complex object security based on incident monitoring. The solving problem approach includes the following steps: building and training a classification model using the C4.5 algorithm, a decision tree creation, risk assessment system development, and incident prediction. The last system is a predicative self-configuring neural system that includes a SCNN (self-configuring neural network), an RNN (recurrent neural network), and a predicative model that allows for determining the risk and forecasting the probability of an incident for an object. We proposed and developed: a mathematical model of a neural system; a SCNN architecture, where, for the first time, the fundamental problem of teaching a perceptron SCNN was solved without a teacher by adapting thresholds of activation functions of RNN neurons and a special learning algorithm; and a predicative model that includes a fuzzy output system with a membership function of current incidents of the considered object, which belongs to three fuzzy sets, namely “low risk”, “medium risk”, and “high risk”. For the first time, we gave the definition of the base class of an object’s prediction and SCNN, and the fundamental problem of teaching a perceptron SCNN was solved without a teacher. We propose an approach to neural system implementation for multiple incidents of complex object security. The results of experimental studies of the forecasting error at the level of 2.41% were obtained.

Analytical Study and Experimental Confirmation of SNA Through Poincaré Maps in a Quasiperiodically Forced Electronic Circuit

2015 ◽  
Vol 25 (08) ◽  
pp. 1530020 ◽  
Author(s):  
A. Arulgnanam ◽  
Awadesh Prasad ◽  
K. Thamilmaran ◽  
M. Daniel
Keyword(s):  
Analytical Study ◽  
Poincaré Map ◽  
Initial Conditions ◽  
Nonlinear Element ◽  
Phase Portraits ◽  
Poincare Map ◽  
Explicit Analytical Solution ◽  
Strange Nonchaotic Attractors ◽  
First Time ◽  
Bifurcation Process

Quasiperiodically forced series LCR circuit with simple nonlinear element is studied analytically and experimentally. To the best of our knowledge, this is the first time that strange nonchaotic attractors (SNAs) are studied analytically. From the explicit analytical solution, the bifurcation process is shown. With a single negative conduction region of the nonlinear element two routes namely, Heagy–Hammel and fractalization routes to the birth of SNA are identified. The analytical analysis are confirmed by laboratory hardware experiments. In addition, for the first time, a detailed stroboscopic Poincaré map is generated experimentally for two different frequencies, for the above two routes, which clearly confirm the presence of SNAs in these two routes. Also, from the experimental data of the corresponding attractors, we quantitatively confirm the presence of SNAs through singular-continuous spectrum analysis. The analytical results as well as experimental observations are characterized qualitatively in terms of phase portraits, Poincaré map, power spectrum, and sensitivity dependance on initial conditions.

scholarly journals A Numerical and Analytical Study of Phase Synchronization in Coupled SC-CNN Based Non-Identical Chaotic Systems

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
H. Shameem Banu ◽  
P.S. Sheik Uduman
Keyword(s):  
Analytical Study ◽  
Chaotic Systems ◽  
Phase Synchronization ◽  
Simulation Method ◽  
Cellular Neural Network ◽  
Phase Portraits ◽  
Complete Synchronization ◽  
Synchronous State ◽  
Explicit Analytical Solution ◽  
Proposed Model

This paper seeks to address the phase synchronization phenomenon using the drive-response concept, in our proposed model, State Controlled Cellular Neural Network (SC-CNN) based on variant of MuraliLakshmanan-Chua (MLCV) circuit. Using this unidirectionally coupled chaotic non autonomous circuits, we described the transition of unsynchronous to synchronous state, by numerical simulation method as well as the results are confirmed by solving explicit analytical solution. In this aspect, the system undergoes the new effect of phase synchronization (PS) phenomenon have been observed before complete synchronization (CS) state. To characterize these phenomena by the phase portraits and the time series plots. Also particularly characterize for PS by the method of partial Poincare section map using phase difference versus time, numerically and analytically. The study of dynamics involved in SC-CNN circuit systems, mainly applicable in the field of neurosciences and in telecommunication fields.

HYPERCHAOS IN RTD-BASED CELLULAR NEURAL NETWORKS

2008 ◽  
Vol 18 (11) ◽  
pp. 3439-3446 ◽  
Author(s):  
FENG-JUAN CHEN ◽  
JI-BIN LI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Lyapunov Exponents ◽  
Cellular Neural Network ◽  
Cellular Neural Networks ◽  
Phase Portraits

In this paper, a hyperchaotic RTD-based cellular neural network is proposed and its hyperchaotic dynamics is demonstrated. The Lyapunov exponents spectrum is presented, and some typical Lyapunov exponents are calculated in a range of parameters. Several important phase portraits are presented as well.

Effects of Low and High Neuron Activation Gradients on the Dynamics of a Simple 3D Hopfield Neural Network

2020 ◽  
Vol 30 (11) ◽  
pp. 2050159 ◽  
Author(s):  
Sami Doubla Isaac ◽  
Z. Tabekoueng Njitacke ◽  
J. Kengne
Keyword(s):  
Neural Network ◽  
Chaotic Behavior ◽  
Equilibrium Points ◽  
Power Spectra ◽  
Hopfield Neural Network ◽  
Basins Of Attraction ◽  
Dynamical Analysis ◽  
Phase Portraits ◽  
Neuron Activation ◽  
Wide Range

In this paper, the effects of low and fast response speeds of neuron activation gradient of a simple 3D Hopfield neural network are explored. It consists of analyzing the effects of low and high neuron activation gradients on the dynamics. By considering an imbalance of the neuron activation gradients, different electrical activities are induced in the network, which enable the occurrence of several nonlinear behaviors. The significant sensitivity of nontrivial equilibrium points to the activation gradients of the first and second neurons relative to that of the third neuron is reported. The dynamical analysis of the model is done in a wide range of the activation gradient of the second neuron. In this range, the model presents areas of periodic behavior, chaotic behavior and periodic window behavior through complex bifurcations. Interesting behaviors such as the coexistences of two, four, six and eight disconnected attractors, as well as the phenomenon of coexisting antimonotonicity, are reported. These singular results are obtained by using nonlinear dynamics analysis tools such as bifurcation diagrams and largest Lyapunov exponents, phase portraits, power spectra and basins of attraction. Finally, some analog results obtained from PSpice-based simulations further verify the numerical results.

n-DOUBLE SCROLLS IN SC-CNN CIRCUIT VIA DIODE-BASED PWL FUNCTION

2006 ◽  
Vol 16 (04) ◽  
pp. 1023-1033 ◽  
Author(s):  
ENIS GÜNAY ◽  
MUSTAFA ALÇI
Keyword(s):  
Neural Network ◽  
Piecewise Linear ◽  
Experimental Studies ◽  
Cellular Neural Network ◽  
Implementation Cost ◽  
Nonlinear Circuit ◽  
Pspice Simulations ◽  
Chaotic Circuits

In this paper n-double scroll generating via diode-based PieceWise-Linear (PWL) circuit in State Controlled Cellular Neural Network (SC-CNN) is presented. It has been shown that by using simple diode-based configurations; alternative nonlinear circuit configurations for chaotic circuits and PWL-based systems can be used in the generation of n-double scrolls. With this study, while the analysis of the nonlinear block in the SC-CNN-based circuit is simplified, the implementation cost of the circuit is also reduced. Pspice simulations are proved with experimental studies.

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

Multicanonical Molecular Dynamics Simulation Study of the Liquid-Solid and Solid-Solid Transitions in Lennard-Jones Clusters

2011 ◽  
Author(s):  
Toshihiro Kaneko ◽  
Kenji Yasuoka ◽  
Ayori Mitsutake ◽  
Xiao Cheng Zeng
Keyword(s):  
Molecular Dynamics ◽  
Heat Capacity ◽  
Transition Temperature ◽  
Peak Position ◽  
Temperature Curve ◽  
Dynamics Simulation ◽  
Lennard Jones ◽  
Dynamics Simulations ◽  
First Time ◽  
Good Agreement

Multicanonical molecular dynamics simulations are applied, for the first time, to study the liquid-solid and solid-solid transitions in Lennard-Jones (LJ) clusters. The transition temperatures are estimated based on the peak position in the heat capacity versus temperature curve. For LJ31, LJ58 and LJ98, our results on the solid-solid transition temperature are in good agreement with previous ones. For LJ309, the predicted liquid-solid transition temperature is also in agreement with previous result.

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