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

References

[1] Wang J. The application of system identification about mathematical modeling in air-conditioning room. Dissertation for the Master’s Degree. Beijing: Beijing University of Civil Engineering and Architecture, 2007, 18-23 (in Chinese)

[2] Wang X F, Lu G Z. System Modeling and Identification. Beijing: Publishing House of Electronics Industry, 2004, 97-121 (in Chinese)

[3] Wang J M, Li X M. Modeling and estimating of the characteristic parameters for the air conditioning room of the VAV system. Computer Simulation, 2002, 19(4): 69-72 (in Chinese)

[4] Zeng J C, Jie Q, Cui Z H. Particle Swarm Optimization. Beijing: Science Press, 2004, 7-17 (in Chinese)

[5] Shi Y, Eberhart R. A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, Piscataway. 1998, 69-73

[6] Wang Z Q, Sun X, Zhang D X. A PSO-based multicast routing algorithm. In: Proceedings of the Third International Conference on Natural Computation, ICNC 2007. 2007, 4: 664-667

[7] Sun H, Zhang Z M, Ge H J. Application of PSO to improve multiple linear regression. Computer Engineering and Applications, 2007, 43(3): 43-45 (in Chinese)

[8] Wu L H, Wang Y N, Zeng Z F, Yuan X F. Parameter estimation of nonlinear systems model based on hybrid particles swarm optimization algorithm. Journal of System Simulation, 2006, 18(7): 1942-1945 (in Chinese)

[9] Wang X, Han C Z, Wang B W. Two new effective bidiagonalization least squares algorithms for nonlinear system identification. Acta Automatica Sinica, 1998, 24(1): 95-111 (in Chinese)

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg