| 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 |
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| 引用本文： | 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].南京气象学院学报,2014,6(2):97-112. |
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| 作者姓名： | 丁锋 汪菲菲 汪学海 |
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| 作者单位： | 江南大学 物联网工程学院, 无锡, 21412;江南大学 控制科学与工程研究中心, 无锡, 214122;江南大学 教育部轻工过程先进控制重点实验室, 无锡, 214122;江南大学 物联网工程学院, 无锡, 21412;江南大学 物联网工程学院, 无锡, 21412 |
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| 基金项目： | 国家自然科学基金（61273194）；江苏省自然科学基金（BK2012549） |
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| 摘 要： | 针对多元伪线性滑动平均系统，讨论了多元增广随机梯度算法，为减小算法的计算量，将系统分解为一些子系统，给出了子系统增广随机梯度算法，利用耦合辨识概念和多新息辨识理论，推导了部分耦合（子系统）增广随机梯度算法、部分耦合（子系统）多新息增广随机梯度算法.进一步将提出的方法推广到多元伪线性自回归滑动平均系统，给出了部分耦合（子系统）广义增广随机梯度算法、部分耦合（子系统）多新息广义增广随机梯度算法.文中分析了多元增广随机梯度算法、部分耦合增广随机梯度算法、部分耦合多新息增广随机梯度算法的计算量.
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| 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 |
| 收稿时间： | 2014/4/8 0:00:00 |
Partially coupled multi-innovation stochastic gradient type identification methods for multivariate pseudo-linear regressive systems |
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| DING Feng,WANG Feifei and WANG Xuehai.Partially coupled multi-innovation stochastic gradient type identification methods for multivariate pseudo-linear regressive systems[J].Journal of Nanjing Institute of Meteorology,2014,6(2):97-112. |
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| Authors: | DING Feng WANG Feifei and WANG Xuehai |
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| Institution: | School of Internet of Things Engineering, Jiangnan University, Wuxi 21412;Control Science and Engineering Research Center, Jiangnan University, Wuxi 214122;Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education), Jiangnan University, Wuxi 214122;School of Internet of Things Engineering, Jiangnan University, Wuxi 21412;School of Internet of Things Engineering, Jiangnan University, Wuxi 21412 |
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| Abstract: | For multivariate pseudo-linear regressive moving average systems,a multivariate extended stochastic gradient(ESG) algorithm is discussed.In order to reduce the computational cost of the identification algorithm,we decompose 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. |
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| Keywords: | parameter estimation recursive identification gradient search least squares auxiliary model identification idea multi-innovation identification theory hierarchical identification principle coupling identification concept multivariate system |
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