Multi-innovation stochastic gradient algorithm for multiple-input single-output systems using the auxiliary model
Multi-innovation stochastic gradient algorithm for multiple-input single-output systems using the auxiliary model
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DOI：
10.1016/j.amc.2009.07.012
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发表时间：
2009-10
期刊：
影响因子：
--
通讯作者：
Yanjun Liu;Yongsong Xiao;Xueliang Zhao
中科院分区：
文献类型：
--
作者：
Yanjun Liu;Yongsong Xiao;Xueliang Zhao
为了减少辨识算法的计算量，提高辨识算法的收敛速度，利用辅助模型辨识思想和多新息辨识理论，针对多输入单输出系统，提出了一种基于辅助模型的多新息随机梯度（AM-MISG）辨识算法。其基本思想是用辅助模型的输出代替信息向量中虚拟子系统的未知输出，提出一种基于辅助模型的随机梯度算法，并通过将标量新息扩展为新息向量，引入新息长度，推导出AM-MISC算法。仿真实例表明，所提算法具有较好的性能.
In order to reduce computational burden and improve the convergence rate of identification algorithms, an auxiliary model based multi-innovation stochastic gradient (AM-MISG) algorithm is derived for the multiple-input single-output systems by means of the auxiliary model identification idea and multi-innovation identification theory. The basic idea is to replace the unknown outputs of the fictitious subsystems in the information vector with the outputs of the auxiliary models and to present an auxiliary model based stochastic gradient algorithm, and then to derive the AM-MISG algorithm by expanding the scalar innovation to innovation vector and introducing the innovation length. The simulation example shows that the proposed algorithms work quite well.