基于量子并行粒子群优化算法的分数阶混沌系统参数估计

分数阶混沌系统参数估计的本质是多维参数优化问题, 其对于实现分数阶混沌控制与同步至关重要. 提出一种基于量子并行特性的粒子群优化新算法, 用于解决分数阶混沌的系统参数估计问题. 利用量子计算的并行特性, 设计出了一种新的量子编码, 使每代运算的可计算次数呈指数增加. 在此基础上, 构建了由量子当前旋转角、个体最优旋转角和全局最优旋转角共同组成的粒子演化方程, 以约束粒子在量子空间中的运动行为, 使算法的搜索能力得到了较大提高. 以分数阶Lorenz混沌系统和分数阶Chen混沌系统的参数估计为例, 进行了未知参数估计的数值仿真, 结果显示本算法具有良好的有效性、鲁棒性和通用性.

Research on particle swarm optimization algorithm with characteristic of quantum parallel and its application in parameter estimation for fractional-order chaotic systems

Parameter estimation for fractional-order chaotic systems is a multi-dimensional optimization problem, which is one of the important issues in fractional-order chaotic control and synchronization. With the characteristic of quantum parallel, a new quantum parallel particle swarm optimization algorithm is proposed for solving the problem of parameter estimation in fractional-order chaotic systems. A new method of quantum coding is presented with quantum parallel characteristic which can make the calculation number of each generation increase exponentially. On the basis of this method, a particle evolution equation composed of quantum current rotation angle, individual optimal rotation angle, and global optimum rotation angle is proposed, which can restraint the behavior of particles in quantum space, and also can improve the search capability of the algorithm. Numerical simulations of the fractional-order Lorenz system and the fractional-order Chen system are conducted and the results demonstrate the effectiveness, robustness and versatility of the proposed algorithm.