On-line optimization of chaotic systems synchronization based on improved particle swarm optimization

This paper studied the feedback parameter optimization which applies the modified particle swarm optimization to realize chaos systems synchronization. However, the object function to be optimized is a multiple hump function, so, in the paper, the random and the ergodicity of the chaotic sequence were applied to initialize particle populations. Because the chaos system is sensitive to the initial value, two chaos systems with same structures and different initial sates will eventually lead to two different trajectories, even if its output error arbitrarily small, This paper used the rolling horizon principle of predictive control to make online optimization of the chaos systems in order to realize the synchronization. Take the chaos system Lorenz for example, we did the numerical simulation to test the feasibility and effectiveness of chaos systems synchronization based on the improved particle swarm optimization. The results indicate that the convergence rate of the system could be improved by the synchronization of the chaos system based on improved particle swarm optimization, which is of good robustness.