Hybrid PSO-SQP for simultaneous identification of structure and parameters in a multivariate system

作者：Jin Qi Bing; Zhang Jian^*; Quan Ling; Cao Li Ting

来源：Control and Decision, 2011, 26(9): 1373-1376+1381.

• Combination optimization
• Efficient method
• High precision
• Hybrid algorithms
• Hybrid Particle Swarm Optimization
• Hybrid PSO
• Inertia weight
• Multivariate systems
• PSO algorithms
• Sequential quadratic programming
• Simulation result
• Simultaneous identification
• SQP algorithm
• Standard PSO
• Structural identification
• System models
• System structures

This paper presents a novel and efficient method to realize both structure and parameter identification in a multivariable system with hybrid particle swarm optimization(PSO) algorithm and the sequential quadratic programming(SQP) algorithm. The conbination of classic mathematical models constitute a system model, so the problem of system structure identification is transformed into a problem of combinatorial optimization. Then by using hybrid PSO algorithm and the SQP algorithm, both structure and parameter identification of the system are realized at the same time. In order to further illustrate the effectiveness of the algorithm, the proposed method is compared with the standard PSO algorithm and the dynamical inertia weight of PSO algorithm. The simulation results show that the proposed hybrid algorithm is reasonable, effective, and has high precision and practicability.

• 出版日期2011
• 单位北京化工大学