摘要
针对模型参数辨识问题,提出了一种基于菌群优化(BSFO)算法的模型参数辨识方法。通过将辨识参数设置为群体细菌在参数空间的位置,并模拟细菌群体觅食的动态行为来实现对参数的寻优,有效地提高了参数辨识的精度和效率。对火电厂热工过程参数辨识的仿真研究验证了本文算法的有效性,结果表明,菌群优化算法能够实现对过程模型参数的有效辨识,仿真结果令人满意。
Aiming at the model parameter identification problem, bacterial swarm foraging for optimization （ BSFO ） algorithm is put forward to identify parameters of the model in this paper. BSFO simulates the social behavior of foraging bacteria, in which the bacteria positions in the parameter spaces are set as the parameters of process model, and the precision and efficiency for parameters identification are improved. The algorithm is applied to thermal process of a power plant. Results indicate that the identification method with BSFO algorithm is valid, and the simulation results are satisfactory.
出处
《自动化技术与应用》
2012年第1期4-7,共4页
Techniques of Automation and Applications
关键词
菌群优化算法
模型辨识
热工过程
bacterial swarm foraging for optimization （ BSFO ）
model identification
thermal process
