基于改进克隆选择算法的统一混沌系统同步控制与参数辨识

提出一种改进的克隆选择算法用于解决混沌系统的参数辨识问题.该算法利用抗体的高频变异和受体编辑两种机制有效平衡算法的全局探索与局部开发,并引入向精英抗体学习策略进一步提高算法的收敛质量.对10个优化问题的实验表明:所提出算法在求解精度、收敛速度以及稳定性方面具有更好的性能.以参数未知统一混沌系统的同步控制为研究对象,合理设计同步控制器,并对同步系统的稳定性进行理论分析.通过对同步比例因子的设置,实现统一混沌系统的完全同步、反同步、投影同步等多种同步方式.仿真实验结果表明该方法能够实现对未知系统参数的精确辨识以及驱动-响应系统的有效同步控制,验证了所提方法的可行性与有效性.

Synchronization Control and Parameter Identification for Unified Chaotic System Based on Improved Clonal Selection Algorithm

An improved clonal selection algorithm was proposed for parameter identification of chaotic systems. Global exploration and local exploration of the algorithm are effectively balanced by two mechanisms of hypermutation and receptor editing. Convergence quality of the algorithm is improved by introducing strategy of learning from elite antibody. Experiments on 10 optimization problems show that the algorithm has good performance in terms of accuracy, convergence speed and stability. Taking synchronization control of unified chaotic system with unknown parameter as research object, synchronization controller was designed reasonably and stability of the synchronization system was analyzed theoretically. By setting synchronization scale factors, a variety of synchronization methods such as complete synchronization, anti-synchronization and projective synchronization of unified chaotic system are realized. Simulation results show that the method realizes accurate identification of unknown system parameter and effective synchronization control of drive-response system. Feasibility and effectiveness of the method are verified.