Abstract
In this paper, we report a novel memristor-based cellular neural network (CNN) without equilibrium points. Dynamical behaviors of the memristor-based CNN are investigated by simulation analysis. The results indicate that the system owns complicated nonlinear phenomena, such as hidden attractors, coexisting attractors, and initial boosting behaviors of position and amplitude. Furthermore, both heterogeneous multistability and homogenous multistability are found in the CNN. Finally, Multisim circuit simulations are performed to prove the chaotic characteristics and multistability of the system.
Highlights
The cellular neural network (CNN) was proposed by Chua and Yang in 1988 for processing signals in real time, which is constituted of an array of the basic circuit units called cells [1]
The state variables of two standard state-controlled cellular neural network (SC-CNN) cells are independent of the output variable, and the state variable of the second cell does not relate to the independent current source
We introduce a memristor-based CNN without equilibrium points, which contains a memristor-based CNN cell and two standard SC-CNN cells
Summary
The cellular neural network (CNN) was proposed by Chua and Yang in 1988 for processing signals in real time, which is constituted of an array of the basic circuit units called cells [1]. A memristor-based CNN without equilibrium points is introduced in this paper, and its dynamical behaviors are investigated. [21] employed a memristor with sine memductance to construct a memristive jerk system This novel memristive jerk system had four line equilibrium sets and periodical initial boosting. [22], a memristormeminductor system was presented, which produced the amplitude, frequency, and position boosting These systems have infinitely many equilibrium points. The initial boosting behaviors of a memristor-based system without equilibrium points are not put forward. We study the initial boosting behaviors in this memristor-based CNN. The system owns homogenous multistability if it generates the same shape coexisting attractors with different positions and amplitudes or even frequencies.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
