Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling

Feng Ding

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Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling

机译：Hammerstein非线性系统建模的多层创新随机梯度算法

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摘要

This paper decomposes a Hammerstein nonlinear system into two subsystems, one containing the parameters of the linear dynamical block and the other containing the parameters of the nonlinear static block, and presents a hierarchical multi-innovation stochastic gradient identification algorithm for Hammerstein systems based on the hierarchical identification principle. The proposed algorithm is simple in principle and easy to implement on-line. A simulation example is provided to test the effectiveness of the proposed algorithm.

机译：本文将Hammerstein非线性系统分解为两个子系统，一个包含线性动态块的参数，另一个包含非线性静态块的参数，并提出了一种基于层次结构的Hammerstein系统的分层多创新随机梯度识别算法。识别原则。该算法原理简单，易于在线实现。提供了一个仿真示例来测试所提出算法的有效性。

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来源
《Applied Mathematical Modelling》 |2013年第4期|1694-1704|共11页
作者
Feng Ding;
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作者单位

Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, PR China Control Science and Engineering Research Center, Jiangnan University, Wuxi 214122, PR China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Parameter estimation; Recursive identification; Least squares; Stochastic gradient; Nonlinear system; Multi-innovation identification;

机译：参数估计;递归识别;最小二乘;随机梯度非线性系统多元创新识别;

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1. Filtering based multi-innovation extended stochastic gradient algorithm for Hammerstein nonlinear system modeling [J] . Ziyun Wang, Yan Wang, Zhicheng Ji Applied Mathematical Modelling . 2015,第18期

机译：Hammerstein非线性系统建模的基于滤波的多创新扩展随机梯度算法
2. A novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems [J] . Mao Yawen, Ding Feng Digital Signal Processing . 2015,第Null期

机译：Hammerstein非线性系统的一种基于数据过滤的多元创新随机梯度算法
3. Hierarchical multi-innovation extended stochastic gradient algorithms for input nonlinear multivariable OEMA systems by the key-term separation principle [J] . Shen Qianyan, Ding Feng Nonlinear dynamics . 2016,第1期

机译：基于关键项分离原理的输入非线性多变量OEMA系统的多层多创新扩展随机梯度算法
4. Multi-innovation gradient parameter estimation algorithms for closed-loop Hammerstein nonlinear systems [C] . Ling Xu, Bingbing Shen, Feng Ding, American Control Conference . 2017

机译：闭环Hammerstein非线性系统的多创新梯度参数估计算法
5. Quadratically constrained least squares identification and nonlinear system identification using Hammerstein/nonlinear feedback models. [D] . Van Pelt, Tobin Hunter. 2000

机译：使用Hammerstein /非线性反馈模型对二次约束最小二乘辨识和非线性系统辨识。
6. FITTING NONLINEAR ORDINARY DIFFERENTIAL EQUATION MODELS WITH RANDOM EFFECTS AND UNKNOWN INITIAL CONDITIONS USING THE STOCHASTIC APPROXIMATION EXPECTATION–MAXIMIZATION (SAEM) ALGORITHM [O] . Sy- Miin Chow, Zhaohua Lu, Hongtu Zhu, -1

机译：随机逼近-最大化（SAEM）算法拟合具有随机效应和未知初始条件的非线性常微分方程模型
7. Auxiliary Model Based Multi-Innovation Stochastic Gradient Identification Algorithm for Periodically Non-Uniformly Sampled-Data Hammerstein Systems [O] . Li Xie, Huizhong Yang 2017

机译：基于辅助模型的周期非均匀采样数据Hammerstein系统的多创新随机梯度识别算法
8. Practical Control Algorithms for Nonlinear Stochastic Systems and Investigations of Nonlinear Filters [R] . Alspach, D. L., Samant, V. S., Sorenson, H. W. 1980

机译：非线性随机系统的实用控制算法及非线性滤波器的研究

Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling

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