A new research of identification strategy based on particle swarm optimization and least square

Abstract

Within the heat and moisture system that is complex in the air-conditioning rooms of large space building, the existence of delay makes the stability cushion reduced, which thereby makes the estimated parameters more complex. In this paper, particle swarm optimization (PSO) is integrated with least square (LS) to improve least squares (short for PSOLS). LS, optimized by PSO, identifies the heat and moisture system parameters of the existence of delay in the air-conditioning rooms by sampling input and output data. In view of this delay system, the identification is an effective solution to nonlinear system which LS can not identify directly. The simulation results show that PSOLS is quite effective, and its global optimization has great potential.

Keywords

least square (LS) / particle swarm optimization (PSO) / system identification / air-conditioning room

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Acknowledgements

This work was supported by the Natural Science Foundation of Beijing, China (No. 0872008), and the sub-topics of the National Natural Science Foundation of China (Grant No. 080709615).

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg