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

Abstract

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.

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

“…CNN (SC-CNN). Many studies have been reported on designing and implementation of chaotic circuits in terms of SC-CNNs 68,69,72 . The advantages of a SC-CNN are that they are inductorless and are RC based circuitry only, thereby leading to the realization of hardware and VLSI implementations 73 .…”

“…We consider a State-Controlled Cellular Neural Network (SC-CNN) based Murali-Lakshmanan-Chua's (MLC) circuit 68,69 . This circuit is constructed by using two CNN cells and external forces including sinusoidal force, biasing and noise.…”

Coexistence of hyperchaos with chaos and its control in a diode-bridge memristor based MLC circuit with experimental validation

“…More investigations with emphasis on various aspects of the circuit have also been tackled. These include strange nonchaotic attractors for computation [15,16], fractional order MLC circuit [17], inductorless design of the MLC based current feedback operational amplifiers (CFOA) [18], and MLC circuit in the frame of cellular neural network [19]. Some recent results on the MLC circuit highlight sliding bifurcation, transient hyperchaos and hyperchaos beats by considering memristor as the nonlinear element in the circuit [20].…”