南京信息工程大学学报Issue(2)：97-112,16.
多元伪线性回归系统部分耦合多新息随机梯度类辨识方法
Partially coupled multi-innovation stochastic gradient type identification methods for multivariate pseudo-linear regressive systems
摘要
Abstract
For multivariate pseudo-linear regressive moving average systems,a multivariate extended stochastic gra-dient(ESG) algorithm is discussed.In order to reduce the computational cost of the identification algorithm,we de-compose a multivariate system into several subsystems,and derive a partially coupled(subsystem) ESG algorithm and a partially coupled( subsystem) multi-innovation ESG algorithm according to the coupling identification concept and the multi-innovation identification theory. Furthermore, we extend these methods to multivariate pseudo-linear autoregressive moving average systems and present a partially coupled( subsystem) generalized extended stochastic gradient ( GESG ) algorithm and a partially coupled ( subsystem ) multi-innovation GESG algorithm. The computational efficiencies of the multivariate ESG algorithm,the partially coupled ESG algorithm and the partially coupled multi-innovation ESG algorithm are analyzed.关键词
参数估计/递推辨识/梯度搜索/最小二乘/辅助模型辨识思想/多新息辨识理论/递阶辨识原理/耦合辨识概念/多元系统分类
信息技术与安全科学引用本文复制引用
丁锋,汪菲菲,汪学海..多元伪线性回归系统部分耦合多新息随机梯度类辨识方法[J].南京信息工程大学学报,2014,(2):97-112,16.基金项目
国家自然科学基金（61273194）；江苏省自然科学基金 （）
