Online Identification and Simulation of Multiple Model Based on Minimum Entropy Clustering

Authors

Xiaopeng Zhao, 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;;
Yongchun Zhang, 2. China Academy of Information and Communications Technology, Beijing 100191, China;

Abstract

Abstract: An online identification algorithm for multiple model based on minimum entropy clustering was investigated. The number of local models and corresponding weights was calculated by the entropy based fuzzy subtractive clustering approach, and the regularity degree of the local system was considered along with the clustering process. Parameters of local models could be estimated online by the weighted recursive least square method. The waste heat recovery Organic Rankine Cycles system was used to demonstrate the algorithm. The result shows the identified multi-model not only can reach an accuracy and reliability identification result, but also has a stronger self-adaptability for uncertain external disturbances.

Recommended Citation

Zhao, Xiaopeng and Zhang, Yongchun (2020) "Online Identification and Simulation of Multiple Model Based on Minimum Entropy Clustering," Journal of System Simulation: Vol. 28: Iss. 6, Article 8.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss6/8

First Page

1306

Revised Date

2015-06-16

DOI Link

https://doi.org/

Last Page

1312

CLC

TP27

Recommended Citation

Zhao Xiaopeng, Zhang Yongchun. Online Identification and Simulation of Multiple Model Based on Minimum Entropy Clustering[J]. Journal of System Simulation, 2016, 28(6): 1306-1312.