基于APSO_WLSSV M算法的Hammerstein ARMAX模型参数辨识

摘要

提出了一种新的使用粒子群算法改进最小二乘支持向量机(adaptive particle swarm optimization,APSO-WLSSVM)的复合算法,应用进化状态估计技术和变异操作改进粒子群算法,使得算法快速收敛于优化目标,具有良好的辨识效果.将所提出的方法与鲁棒最小二成向量机、最小二成相量机方法进行数值例子比较研究,结果证明了所提出的APSO-WLSSVM 方法的有效性.%A new adaptive particle swarm optimization is proposed to improve the least squares support vector machine weighted least squares support vectort vector machine(APSO-WLSSVM)composite algorithm.The application of evolutionary state esti-mation techniques and mutation operation are applied to improve the particle swarm optimization(PSO)which promote the algo-rithm convergence to the optimization target rapidly with a good recognition effect.In the numerical simulation,the proposed methods are compared with WLSSVM and LSSVM methods.It is proven that the effectiveness of the proposed method.