Volterra series identification method based on adaptive ant colony optimization
Li Zhi-nong1; Tang Gao-song2; Xiao Yao-xian1; Wu Guan-hua1
1. Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchang, 360063;2. School of Mechanical Engineering, ZhengZhou University, ZhengZhou 450001, China
Abstract：A Volterra series identification method based on Adapt Ant Colony Optimization (AACO) algorithm is proposed. In the proposed method, the Volterra kernel is identified by ant colony optimization algorithm. the parameters of the ant colony algorithm can be adaptively adjusted as the incre.ase of evolution number. At the same time, the proposed method are compared with the Volterra kernel identification method based on basic ant colony optimization (ACO). The simulation result shows that the proposed method and ACO identification method have good identification accuracy, convergence stability and robust anti-noise performance whether in the noise-free or noise environment. However in the convergence speed, the proposed method is supirior to the ACO identification method.
李志农;唐高松;肖尧先;邬冠华. 基于自适应蚁群优化的Volterra核辨识算法研究[J]. , 2011, 30(10): 35-38.
Li Zhi-nong;Tang Gao-song;Xiao Yao-xian;Wu Guan-hua. Volterra series identification method based on adaptive ant colony optimization. , 2011, 30(10): 35-38.
2011, Vol. 30 
