State Controlled Cellular Neural Network Research Articles

Realization of all logic gates and memory latch in the SC-CNN cell of the simple nonlinear MLC circuit

We investigate the State-Controlled Cellular Neural Network framework of Murali–Lakshmanan–Chua circuit system subjected to two logical signals. By exploiting the attractors generated by this circuit in different regions of phase space, we show that the nonlinear circuit is capable of producing all the logic gates, namely, or, and, nor, nand, Ex-or, and Ex-nor gates, available in digital systems. Further, the circuit system emulates three-input gates and Set–Reset flip-flop logic as well. Moreover, all these logical elements and flip-flop are found to be tolerant to noise. These phenomena are also experimentally demonstrated. Thus, our investigation to realize all logic gates and memory latch in a nonlinear circuit system paves the way to replace or complement the existing technology with a limited number of hardware.

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

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.

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

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.

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

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.

Symmetrical coexisting attractors and extreme multistability induced by memristor operating configurations in SC-CNN

Recently, a striking dynamical phenomenon characterized as coexisting attractors in nonlinear systems has been extensively studied. In this study, a novel memristive chaotic system is proposed by introducing a universal memristor emulator into a state controlled cellular neural network (SC-CNN), in which the emulator can be configured as an incremental or a decremental memristor. When the system parameter is varied, interestingly, a series of symmetrical attractors are induced by interchanging the operating configurations of the used emulator in the presented system. Furthermore, the dynamical behavior depending on the memristor initial values is also investigated and a more complex phenomenon of the symmetrical extreme multistability is revealed. The complex dynamical characteristics of the system are investigated by phase portraits, bifurcation diagrams, Lyapunov exponents. Pspice simulations and hardware experiments are included to verify numerical simulations.

Switched State Controlled-CNN: An Alternative Approach in Generating Complex Systems with Multivariable Nonlinearities Using CNN

It has been already shown in the literature that extended State Controlled-Cellular Neural Network (SC-CNN) could be used to approximate the behavior of complex dynamics with multivariable nonlinearities by modifying the nonlinearity. In this study, a different approach to imitating the multivariable nonlinearities in complex dynamical systems by switching the states of SC-CNN is given. For this purpose, first of all, Rössler type system dynamics are generated by switching the states of Cellular Neural Networks (CNNs). In the second study, Sprott-94-f type system is presented. All systems are numerically and experimentally investigated in order to verify the convenience of the standpoint.

Strange non-chaotic attractors in a state controlled-cellular neural network-based quasiperiodically forced MLC circuit

In this paper, we report the dynamical transitions to strange non-chaotic attractors in a quasiperiodically forced state controlled-cellular neural network (SC-CNN)-based MLC circuit via two different mechanisms, namely the Heagy–Hammel route and the gradual fractalisation route. These transitions were observed through numerical simulations and hardware experiments and confirmed using statistical tools, such as maximal Lyapunov exponent spectrum and its variance and singular continuous spectral analysis. We find that there is a remarkable agreement of the results from both numerical simulations as well as from hardware experiments.

Multi-Scroll Chaotic Attractors in SC-CNN via Hyperbolic Tangent Function

A State Controlled-Cellular Neural Network (SC-CNN) based chaotic model for generating multi-scroll attractors via hyperbolic tangent function series is proposed in this paper. After presenting the double scroll generation, the presented SC-CNN system is used in multi-scroll chaotic attractor generation by adding hyperbolic tangent function series. By using equilibrium analysis and their stability such as Lyapunov exponent analysis, bifurcation diagrams and Poincaré map, the dynamical behaviors of the proposed system are theoretically analyzed and numerically investigated.

Realization of current-mode SC-CNN-based Chua’s circuit

A current-mode State-Controlled Cellular Neural Network (SC-CNN) cell based on current conveyors (CCIIs) and multi-output current conveyors (MOCCII) is presented in this paper. Different from the voltage-mode SC-CNN cell whose state variable is voltage signal, the current-mode SC-CNN cell takes current signal as its state variable. A current-mode SC-CNN-based chaotic circuit is developed by using the suitable connection of three current-mode SC-CNN cells. When compared with the existing voltage-mode chaotic circuits, the proposed current-mode chaotic circuit can operate at higher frequency ranges. In addition to this, state currents can be directly obtained at high impedance outputs of MOCCIIs. Furthermore, all elements in this circuit are grounded, thus it is very suitable for integrated circuit (IC) fabrication. Pspice simulations are given to demonstrate the dynamical behaviors of the proposed circuit and its high frequency performance.

Dynamics and SC-CNN circuit implementation of a periodically forced non-smooth mechanical system

A two-degree-of-freedom periodically forced system with multiple motion limiting constraints is considered. The incidence relation between dynamics and key parameters (clearance, constraint stiffness and forcing frequency) is numerically studied by selecting different values of constraint stiffness and finely scanning the (forcing frequency, clearance)–parameter plane. Two cases, associated with the non-prepressing and prepressing constraints, are considered in the research. The fundamental group of impact motions with an excitation period and the different impact number is studied with emphasis on the generation mechanism. The complicated and regular transition characteristics between adjacent fundamental impact motions are analyzed by their existence domains and bifurcation boundaries in the (forcing frequency, clearance)–parameter plane. The influence of the constraint parameters on multiformity and transitional characters of periodic–impact motions of the system is studied in relatively large parameter spaces. An electronic circuit in the framework of state-controlled cellular neural network (SC-CNN) is designed for experimental verification of dynamic mechanical behaviors of the non-smooth system driven by periodical exciting forces. Real electronic circuit, based on four SC-CNN cells, is made by using printed circuit board, and the oscilloscope output of phase portraits of electrical waveforms generated by the circuit itself is experimentally observed. A good agreement among the numerical results of the mechanical model, the electronic design simulation of the circuit and the real oscilloscope outputs of hardware implementation is confirmed. The results fully show that the modeling method using the strategy based on SC-CNN is suitable for the modeling of the periodically forced systems with multiple motion limiting constraints. The high availability and reliability of the electronic circuit, based on the SC-CNN framework, are verified by the contrast analyses.

Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection

According to the problems of current distributed architecture intrusion detection systems (DIDS), a new online distributed intrusion detection model based on cellular neural network (CNN) was proposed, in which discrete-time CNN (DTCNN) was used as weak classifier in each local node and state-controlled CNN (SCCNN) was used as global detection method, respectively. We further proposed a new method for design template parameters of SCCNN via solving Linear Matrix Inequality. Experimental results based on KDD CUP 99 dataset show its feasibility and effectiveness. Emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation which allows the distributed intrusion detection to be performed better.

Adaptive Synchronization of Chaotic SC-CNN with Uncertain State Template

The problem of synchronization of chaotic State Controlled Cellular Neural Network (SC-CNN) with uncertain state template is investigated. In detail, the following three cases are solved: firstly, synchronization of two identical chaotic SC-CNNs with uncertain state template, secondly, synchronization of two nonidentical chaotic SC-CNNs with all uncertain state templates, and, thirdly, synchronization between chaotic SC-CNN with uncertain state template and different uncertain parameter chaotic systems. The controllers and update laws proposed in each case are proved closely based on Lyapunov stability theory. In addition, some illustrative corresponding examples are presented to demonstrate the effectiveness and usefulness of the proposed control laws.

Hyperchaos in SC-CNN based modified canonical Chua’s circuit

In this paper, a state-controlled cellular neural network (SC-CNN)-based hyperchaotic circuit is implemented for classical modified canonical Chua’s circuit. The proposed system is modeled by using a suitable connection of four state-controlled generalized CNN cells, while the stability of the circuit is studied by determining the eigenvalues of the stability matrices, the system parameter is varied, and the dynamics as well as the onset of chaos and hyperchaos followed by a period-three doubling bifurcation has been studied through numerical analysis of the generalized SC-CNN equations and real-time experiments. We further validate our findings, the chaotic and hyperchaotic dynamics, characterized by two positive Lyapunov exponents and Lyapunov dimension, is described by a set of four coupled first-order generalized SC-CNN equations. This has been investigated extensively not only analyzing by computer simulation but also demonstrating by laboratory experiments. The experimental results such as phase portraits, Poincare surface sections and power spectra are in good agreement with those of numerical computations.

Chaos synchronization and transmission of information in coupled SC-CNN-based canonical Chua’s circuit

In this paper, an experimental implementation of State Controlled Cellular Neural Network (SC-CNN)-based canonical Chua’s circuit is presented. Two such electronic circuits are realized. One of the circuits is configured as driver or transmitter, and other circuit is configured as response or receiver system. Synchronization of chaos between the driver and the driven system is demonstrated. The conditions under which complete synchronization is ensured are derived by applying a strategy based on the Master Stability Function and Lyapunov function. We show that transmitter and receiver systems consisting of cascaded SC-CNN-based canonical Chua’s oscillator can synchronize when they are coupled using direct information from the transmitter into the receiver. The communication performances of different information signals such as sinusoidal, square, and speech signals are demonstrated by real-time hardware experiments. The experimentally observed results are in agreement with the numerically obtained results, and they closely match with each other.

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

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.

An experimental study on SC-CNN based canonical Chua’s circuit

In this paper, a State Controlled Cellular Neural Network (SC-CNN) based autonomous canonical Chua’s circuit is presented. The proposed system is modeled by using suitable connection of three simple state controlled generalized CNN cells. The stability of the circuit is studied by determining the eigenvalues of the stability matrices, while the dynamics as well as onset of chaos have been studied through real time experiments and numerical analysis of the generalized SC-CNN equations. The experimental results such as phase portraits and power spectra are in good agreement with those of numerical computations.

A NEW AUTONOMOUS CHAOS GENERATOR FROM STATE CONTROLLED-CELLULAR NEURAL NETWORKS

In this study, a new autonomous chaos generator is presented via State Controlled-Cellular Neural Networks (SC-CNNs). From the numerical and circuit simulations, it has been shown that the proposed SC-CNN consisting of three cells can be easily used in chaotic applications as a chaos generator. After presenting the double scroll generation, the presented SC-CNN system is used either in multi-scroll chaotic attractor generation by adding a trigonometric function generator and or to construct a SC-CNN based chaotic masking system with feedback algorithm.

Synchronization Method of Coupling Coefficient of Linear and Nonlinear in SC-CNN(State-Controlled Cellular Neural Network)

Recently, the research of security and its related problems has been received great interested. The research for hper-chaos systems and its synchronization are actively processing as one of method to apply to secure and cryptography communication. In this paper, we propose the synchronization method by coupling coefficient of linear and nonlinear in order to accomplish the synchronization of hyper-chaos system that organized by SC-CNN(State-Controlled Cellular Neural Network). We also verify and confirm the result of synchronization between entire transmitter and receiver, and each subsystem in transmitter and receiver through the phase portrait and difference of time-series by the computer simulation.

A NEW WAY OF GENERATING N-SCROLL ATTRACTORS VIA TRIGONOMETRIC FUNCTION

In this work, a way of generating n-scroll attractors in State Controlled-Cellular Neural Network (SC-CNN) using a trigonometric function is introduced. In spite of the studies on generation of n-scrolls cited in literature, there is no need to add break points in the nonlinear function of the system. Also, wide number of scrolls can be generated by modifying only one variable.

BREAKING A SC-CNN-BASED CHAOTIC MASKING SECURE COMMUNICATION SYSTEM

This paper studies the security of a chaotic cryptosystem based on Chua's circuit and implemented with State Controlled Cellular Neural Networks (SC-CNN). Here, we prove that the plaintext can be retrieved by bandpass filtering of the ciphertext or by using an imperfect decoder with wrong receiver parameters. In addition, we find that the key space of the system can be reduced notably, and the required resolution of the parameter values to recover a meaningful plaintext is as coarse as 5%, easing a brute-force attack. The system parameters can be determined with high precision through the analysis of the decoding error produced by the mismatch between the parameters of receiver and transmitter.