摘要
对于未知时延的多输入单输出(MISO)系统,借助分离性原理,推导出迭代的可分离的非线性最小二乘(SNLS)辨识方法.为降低收敛于局部最小的可能性,利用全局优化理论,推导了全局可分离的非线性最小二乘(GSNLS)辨识方法;为消除强观测噪声所引起的参数估计的偏差,将GSNLS方法调整为一新颖的全局可分离的非线性多新息递推最小二乘(GSNMIRLS)辨识方法,仿真实验验证了算法的有效性.
For MISO systems with multiple unknown time delays,an iterative separable nonlinear least-squares （SNLS） identification method is proposed by means of the separable principle.Then a global separable nonlinear least-squares （GSNLS） identification method which estimates the time delays and transfer function parameters separably is derived to reduce the possibility of convergence to a local minimum by using the global optimization theory.Furthermore,the GSNLS method is modified to a novel global separable nonlinear multi-innovation recursive least-squares identification （GSNMIRLS） method to eliminate the biases of the estimates in the presence of high measurement noise.The simulation results show the theoretical results.
出处
《控制与决策》
EI
CSCD
北大核心
2010年第1期93-98,共6页
Control and Decision
基金
国家自然科学基金项目(60874037)
关键词
递推辨识
时延
非线性最小二乘法
多新息
全局优化
Recursive identification
Time delay
Nonlinear least-squares method
Multi-innovation
Global optimization
