基于CLPSO算法的结构系统识别

系统识别问题可以转化成高维多模优化问题。针对基本粒子群优化在分析此类问题时容易出现早熟收敛从而导致局部优化和产生较大误差,提出将基于综合学习策略粒子群优化算法(CLPSO)应用于结构参数识别。由于该方法能够保持群体的多样性,因此可以避免早熟收敛。利用该方法在测量数据不完备且有噪声污染的条件下,同时在没有系统质量和刚度等先验信息的情况下对结构系统进行了识别,通过数值模拟以及对某真实结构进行分析,验证了该方法对结构系统识别的有效性。

Structural System Identification Using Comprehensive Learning Particle Swarm Optimization Algorithm

Abstract System identification can be formulated as a multimodal optimization problem with high dimension. The original particle swarm optimization (PSO) usually suffers from premature convergence tending to get stuck to local optima and low solution precision while solving these complex multimodal problems. In order to solve this problem, a comprehensive learning particle swarm optimization (CLPSO) method was utilized to estimate parameters of structural systems. This variant of PSO enables the diversity of the swarm to be preserved to discourage premature convergence. The effectiveness of the proposed method is evaluated through the numerical analysis and an application to a real building under conditions including limited measurement data, noise polluted signals, and no prior knowledge of mass, damping, or stiffness.