Parameter identification in chaotic system based on feedback teaching-learning-based optimization algorithm
LI Ruiguo1, ZHANG Hongli1, WANG Ya2
1. School of Electrical Engineering, Xinjiang University, Urumqi Xinjiang 830047, China;
2. School of Mechanical Engineering, Xinjiang University, Urumqi Xinjiang 830047, China
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.
李瑞国, 张宏立, 王雅. 基于反馈教学优化算法的混沌系统参数辨识[J]. 计算机应用, 2015, 35(5): 1367-1372.
LI Ruiguo, ZHANG Hongli, WANG Ya. Parameter identification in chaotic system based on feedback teaching-learning-based optimization algorithm. Journal of Computer Applications, 2015, 35(5): 1367-1372.
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