基于反馈教学优化算法的混沌系统参数辨识

针对传统智能优化算法对混沌系统参数辨识精度低、速度慢的问题,提出一种基于反馈教学优化算法的混沌系统参数辨识的新方法.该方法以教学优化算法为基础,在教授-学习阶段之后加入反馈阶段,同时将参数辨识问题转化为参数空间上的函数优化问题.分别以三维二次自治广义Lorenz系统、Jerk系统和Sprott-J系统为待辨识模型,对粒子群优化算法、量子粒子群优化算法、教学优化算法及反馈教学优化算法进行了对比实验,反馈教学优化算法辨识误差为零,搜索次数明显减少.仿真结果表明,反馈教学优化算法明显提高了混沌系统参数辨识精度和速度,验证了该算法的可行性和有效性.

Parameter identification in chaotic system based on feedback teaching-learning-based optimization algorithm

Concerning low precision and slow speed of traditional intelligent optimization algorithm for parameter identification in chaotic system, a new method of parameter identification in chaotic system based on feedback teaching-learning-based optimization algorithm was proposed. This method was based on the teaching-learning-based optimization algorithm, where the feedback stage was introduced at the end of the teaching and learning stage. At the same time the parameter identification problem was converted into a function optimization problem in parameter space. Three-dimensional quadratic autonomous generalized Lorenz system, Jerk system and Sprott-J system were taken as models respectively, intercomparison experiments among particle swarm optimization algorithm, quantum particle swarm optimization algorithm, teaching-learning-based optimization algorithm and feedback teaching-learning-based optimization algorithm were conducted. The identification error of the feedback teaching-learning-based optimization algorithm was zero, meanwhile, the search times was decreased significantly. The simulation results show that the feedback teaching-learning-based optimization algorithm improves the precision and speed of the parameter identification in chaotic system markedly, so the feasibility and effectiveness of the algorithm are well demonstrated.