Abstract

An amplitude controllable hyperjerk system is constructed for chaos producing by introducing a nonlinear factor of memristor. In this case, the amplitude control is realized from a single coefficient in the memristor. The hyperjerk system has a line of equilibria and also shows extreme multistability indicated by the initial value-associated bifurcation diagram. FPGA-based circuit realization is also given for physical verification. Finally, the proposed memristive hyperjerk system is successfully predicted with artificial neural networks for AI based engineering applications.

Highlights

  • Memristor brings the nonlinear factor with memory function [1,2,3,4]. e discovery of the memristor gives the possibility of the circuit with brain-like natural memory

  • In 2008, HP Company has triggered an upsurge in the research of memristor, among which memristive chaotic system is the main branch [5,6,7]. e amplitude control in the memristive system has aroused great interest in chaos producing

  • Li explained the mechanism of amplitude control in chaotic systems [13], and the partial amplitude control was studied by Li et al and Gu et al [14, 15]

Read more

Summary

Introduction

Memristor brings the nonlinear factor with memory function [1,2,3,4]. e discovery of the memristor gives the possibility of the circuit with brain-like natural memory. E amplitude control in the memristive system has aroused great interest in chaos producing. E amplitude control with one parameter is still a challenge and attractive in chaos application. Motivated by the above discussions, in this work, the newly proposed memristive hyperjerk chaotic system with amplitude control is implemented by FPGA. In order to control the dynamics of chaotic systems, some research tried to predict the data outputting by various classes of optimization. A memristor is introduced into a jerk structure for chaos producing with amplitude control and a line of equilibria.

Memristive Hyperjerk Chaotic System and Basic Analysis
FPGA-Based Implementation
System Prediction with Artificial Neural Network
Findings
Conclusion
Full Text
Published version (Free)

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 call