Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition

Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition
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基于关键项分离原理和模型分解的Hammerstein控制自回归系统多创新随机梯度辨识算法

DOI:
10.1155/2013/596141
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发表时间:
2013-10
影响因子:
--
通讯作者:
Ding, Rui
Ding, Rui
中科院分区:
--
文献类型:
--
作者:
Hu, Huiyi;Xiao Yongsong;Ding, Rui

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将一个输入非线性系统分解为两个子系统,一个子系统包含系统模型参数,另一个子系统包含噪声模型参数,基于关键项分离原理和模型分解,提出了一种多新息随机梯度算法,以提高算法的收敛性 随机梯度算法的速度。关键项分离原理可以简化输入非线性系统的辨识模型,分解技术可以提高辨识算法的计算效率。仿真结果表明,该算法是一种有效的IN-CAAR系统参数估计方法。
An input nonlinear system is decomposed into two subsystems, one including the parameters of the system model and the other including the parameters of the noise model, and a multi-innovation stochastic gradient algorithm is presented for Hammerstein controlled autoregressive autoregressive (H-CARAR) systems based on the key term separation principle and on the model decomposition, in order to improve the convergence speed of the stochastic gradient algorithm. The key term separation principle can simplify the identification model of the input nonlinear system, and the decomposition technique can enhance computational efficiencies of identification algorithms. The simulation results show that the proposed algorithm is effective for estimating the parameters of IN-CARAR systems.
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