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A secure transmission application of the State Controlled Cellular Neural Network (SC–CNN)-based circuit is presented. Since the SC–CNN-based circuit has feedback connections between the cells, it is potentially very suitable for realizing a chaotic masking system with feedback algorithm. So, we have constructed a chaotic masking system with feedback using the SC–CNN-based circuit. PSpice simulation experiments verify that the proposed SC–CNN-based secure communication system exhibits a good performance for a wide range of amplitude and spectral characteristics of the information signal.

In this study experimental results of State Controlled Cellular Neural Network (SC-CNN)-based chaotic masking system are presented. By means of this study, the chaotic masking system with feedback algorithm in SC-CNN-based circuit is experimentally proved.

A CNN-based nonautonomous chaotic oscillator circuit design is presented. Murali–Lakshmanan–Chua circuit, known as MLC circuit, is modeled by using CNN cells. The circuit implementation is supported by an eigenvalue study of the introduced system. The proposed model gives an alternative to MLC circuit with inductorless RC-based circuit realization.

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.