中国电机工程学报Issue(32)：5779-5787,9.DOI:10.13334/j.0258-8013.pcsee.2014.32.012
基于大数据和双量子粒子群算法的多变量系统辨识
Multivariable System Identification Based on Double Quantum Particle Swarm Optimization and Big Data
韩璞 1袁世通1
作者信息
- 1. 河北省发电过程仿真与优化控制工程技术研究中心 华北电力大学，河北省 保定市 071003
- 折叠
摘要
Abstract
To overcome the drawback of imprecise quantification for each subsystem during the process of multivariable system identification with intelligent algorithm and historical data, an effective optimized data parallel computing solution was proposed. In order to improve the convergence speed and precision of quantum particle swarm optimization (QPSO) during the identification, a new improved QPSO algorithm named double quantum particle swarm optimization (D-QPSO) was presented. The particle’s encoding mechanism and the evolutionary search strategy were simultaneously quantized in D-QPSO algorithm. Several benchmark test functions were used to test the proposed D-QPSO algorithm, which verified that the new algorithm was superior to standard PSO and QPSO in search capabilities. Finally, the proposed scheme was used in transfer function identification of the thermal power plant load control system, and the parameters of the model were estimated by the D-QPSO algorithm and historical data. The identification results laid the foundation for load control system design and optimization.关键词
量子粒子群算法/双量子粒子群算法/数据挖掘/多变量系统/系统辨识Key words
quantum particle swarm optimization/double quantum particle swarm optimization/data mining/multivariable system/system identification分类
信息技术与安全科学引用本文复制引用
韩璞,袁世通..基于大数据和双量子粒子群算法的多变量系统辨识[J].中国电机工程学报,2014,(32):5779-5787,9.
