基于状态观测器的历史数据建模方法研究

doi:10.16182/j.issn1004731x.joss.201801003

摘要/Abstract

摘要： 针对常规历史数据建模法无法克服扰动对建模精度影响的问题,提出一种基于状态观测器的历史数据建模方法。选取历史数据中具有从动态到稳态特征的数据段作为建模数据,依据终点稳态值对数据进行零初始化处理,将该数据段分为两段,一段数据用来对预估模型的状态进行跟踪,并将最终的观测状态作为预估模型新的初始状态;另一段数据用来对预估模型参数准确程度进行评价,辅以粒子群算法进行模型参数寻优,该方法具有常规历史数据建模的优点,可有效消除扰动对建模过程的影响。

关键词: 建模, 历史数据, 状态观测器, 扰动

Abstract: Aiming at the problem that the conventional historical data modeling method can not overcome the influence of disturbance on modeling accuracy, a historical data modeling method based on state observation is proposed. In this method, the data with the characteristic from dynamic to steady state are selected from the historical data as the modeling data, which is subjected to zero initialization processing based on the final steady state value. The processed data is then divided into two segments. The first segment is used to track the state of the prediction model, and the final observed state is used as the new initial state of the prediction model. The second segment is used to evaluate the accuracy of the estimated model parameters, and the particle swarm optimization is employed to find the optimal model parameters. The method not only has the advantages of the conventional historical data modeling, but also can effectively eliminate the influence of disturbance on the modeling process.

Key words: modeling, historical data, state observer, disturbance

中图分类号:

TP391.9

董泽, 尹二新. 基于状态观测器的历史数据建模方法研究[J]. 系统仿真学报, 2018, 30(1): 18-27.

Dong Ze, Yin Erxin. Historical Data Modeling Based on State Observation[J]. Journal of System Simulation, 2018, 30(1): 18-27.

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