基于协同PSO算法的非均匀采样系统辨识

摘要

对于非均匀采样数据的双率系统，运用提升技术，得到系统离散状态空间模型，变换得到相应的传递函数模型。讨论在有色噪声和白噪声的干扰下，提出了利用协同粒子群（Cooperative Particle Swarm Optimization，CPSO）的新颖算法，通过实验仿真对比传统的算法和协同PSO算法的精度和鲁棒性，证明新型算法的有效性和合理性。%For one type of non-uniformly sampled data of dual-rate system, by using the lifting technique the state space models of the multirate systems are got and thus the corresponding transfer functions are obtained. An asynchronous pattern from analyzing on biologic character of particle swarm optimization is proposed, and a novel method is studied for dual-rate system identification which is based on the improved CPSO(Cooperative Particle Swarm Optimization, CPSO)under different noise models. To prove the validity and rationality of the new algorithm, this paper compares the differences of many traditional algorithms and CPSO algorithm in accuracy and robustness by experimental simulation.