基于量子粒子群算法的混沌系统同步控制及参数辨识

针对异结构且参数未知混沌系统的同步控制，设计同步控制器并对误差系统的稳定性进行理论分析.通过合理选择优化目标函数，利用量子粒子群算法高效的全局寻优能力，实现混沌系统的未知参数辨识，有效降低同步控制器的开发难度.以Rössler混沌系统与Lorenz混沌系统的异结构同步为研究对象，进行数值仿真实验，结果表明该方法能够实现驱动-响应系统的快速混沌同步及系统未知参数的准确辨识，并验证该方法的可行性和有效性.

Synchronization and Parameter Identification of Chaotic Systems Based on Quantum-Behaved Particle Swarm Optimization

For synchronization cotrol of chaotic systems with different structures and unkown parameters, synchronization controller is designed and stability of error system is analyzed theoretically. With reasonably selected optimization objective function and quantum-behaved particle swarm optimization, unknown parameters identification of chaotic system are realized, which reduces effectively development difficulty of the synchronization controller. Synchronization between Rössler chaotic system and Lorenz chaotic system is studied, and numerical experiments are carried out. It shows that the proposed method achieves fast synchronization of drive-response system and accurate identification of unknown system parameters, which verifies feasibility and effectiveness of the method.