Chaos synchronization in a 6-D hyperchaotic system with self-excited attractor

Abstract

This paper presented stability application for chaos synchronization using a 6-D hyperchaotic system of different controllers and two tools: Lyapunov stability theory and Linearization methods. Synchronization methods based on nonlinear control strategy is used. The selecting controller's methods have been modified by applying complete synchronization. The Linearization methods can achieve convergence according to the of complete synchronization. Numerical simulations are carried out by using MATLAB to validate the effectiveness of the analytical technique.

Encrypting an audio file based on integer wavelet transform and hand geometry

“…Encryption/steganography in transform domain gives greater security [25,26]. Other researchers have also suggested using chaotic and hyperchaotic systems in encryption to increase data security [27][28][29][30][31][32][33][34][35][36][37][38][39][40].…”

Projective and hybrid projective synchronization of 4-D hyperchaotic system via nonlinear controller strategy

“…For simulation, the MATLAB version R2017a is adopted to solve the differential equation of controlled error dynamical system (8), system (12) and system (17) for PS and controlled error dynamical system (21), system (25) for HPS based on fourth-order Runge-Kutta scheme with time step ℎ = 0.01 and the and the initial values of the drive system and the response system are following (3.2, 8.5, 3.5, 2.0) and (−3. 2, −8.…”

“…In projective and HPS processes, various strategies have been introduced to stabilize dynamic error systems, including adaptive control [16], active control, nonlinear control [17][18][19][20] and linear feedback control [21][22][23]. Among many control strategies, the nonlinear control strategy has been continuously for TELKOMNIKA Telecommun Comput El Control 1013 which great interest to many scientists, due to its effectiveness, reliability, and widely has been used as a single powerful strategy for synchronizing different class of the nonlinear dynamic systems [24,25]. But, the control input design should be based on the functions of the controlled system according to the traditional nonlinear control.…”

The best parameters selection using pso algorithm to solving for ito system by new iterative technique

“…The particle swarm optimaiztion PSO algorithm also has qualities and features that make it distinctly efficient as it does not require improvement of any scalable information of the function and uses simple logical factors, compared to other algorithms, we find it characterized by its speed, low cost and accuracy [22][23][24]. The pso algorithm is suitable for solving linear, non-linear, intermittent and continuous variables, so that is has been used as a robust method to solve optimization problems in a wide variety of applications [25][26][27], We work to reduce the error to a minimum in the numerical method used the new iterative method and thus arrive at the nearest approximate and analytical solution to the exact solution of the system Ito. The following is a flowchart showing the PSO algorithm in Figure 1 [28,29]: In this article we will improve the results and minimize the error so that it converges to the exact solution of the non-linear Ito system by solving the system with the new iterative method and then proposing a new strategy between the new iterative method with the Pso algorithm.…”