摘要
针对多输入多输出(MIMO)热工过程的非线性、强耦合、变工况及参数时变等特点,提出了一种基于系统输入输出数据和模糊自适应竞争聚类的模型辨识新方法.该方法首先依据系统的各个典型运行工况,使用模糊自适应竞争聚类对输入输出数据进行聚类划分,并对T-S模糊模型进行结构辨识,以确定系统的模型结构和参数;然后采用最小二乘递推算法对模型后件参数进行辨识,同时对结构辨识参数进行精确修正.将所提出的模型辨识方法用于锅炉-汽轮机非线性系统的模型辨识,仿真结果验证了该方法的有效性.
Considering the nonlinear, strong coupling, parameters time-varying characteristics and variable work conditions of multi input multi output（MIMO） thermal process, a new model identification method is proposed based on input-output data of the system and fuzzy adaptive competitive clustering, with which the input-output data are firstly clustered according to typical work conditions, while the T-S model structure identified so as to determine the antecedent parameters of the system; then, the consequent parameters of the fuzzy rules are identified using recursive least squares, while the antecedent parameters modified exactly. Application results on the nonlinear model of a boiler-turbine unit prove the model identification method to be effective.
出处
《动力工程学报》
CAS
CSCD
北大核心
2012年第10期798-803,共6页
Journal of Chinese Society of Power Engineering
基金
吉林省教育厅基金资助项目(2010-335)
关键词
系统辨识
T—S模糊模型
自适应竞争聚类
热工过程
锅炉
汽轮机
system identification
T-S fuzzy model
adaptive competitive clustering
thermal process
boiler
turbine
