New Development in Subspace System Identification via Realization Theory
实现论子空间系统辨识的新进展
基本信息
- 批准号:17560389
- 负责人:
- 金额:$ 2.27万
- 依托单位:
- 依托单位国家:日本
- 项目类别:Grant-in-Aid for Scientific Research (C)
- 财政年份:2005
- 资助国家:日本
- 起止时间:2005 至 2007
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The objective of this research project is to develop new subspace identification methods for multivariable systems based on deterministic and stochastic realization theories. The following are the results of the research for the last three years.1. The role of LQ decomposition in subspace methods is clarified by showing that each column of L-matrix is a pair of input-output vectors generated by given input-output data [R6, R8].2. Subspace identification algorithms of identifying closed-loop systems are derived using the ORT (orthogonal decomposition based subspace identification) method by using joint input-output approach [J1] and by the two-stage method [J5, R1]. Moreover, the two stage method is applied to the identification of industrial process [R5].3. Anew stochastic realization algorithm is derived by using LQ decomposition in Hilbert space [J3], and it is successfully applied to the closed-loop stochastic realization [R7]. Also, a finite-interval stochastic balanced realization is analyzed and it is shown that the finite-interval balanced realization algorithm provides stable minimum-phase models under the assumption that an exact finite covariance sequence is available [J4, R2].4. Identification methods for error-in-variables models are developed by using J-spectral factorization And H-infinity identification technique [R4, R9]. Moreover, new identification algorithms are derived by combining the EM algorithm and ORT method in order to cope with outliers in observed data [J2, R3].5. An English monograph for subspace methods for system identification is published from Springer-Verlag, which is suitable for a textbook of graduate students and of applied scientists [B1].
本研究计画的目标是发展新的子空间辨识方法,以多变量系统的确定性与随机性实现理论为基础。以下是过去三年的研究结果。通过证明L-矩阵的每一列是由给定的输入输出数据生成的一对输入输出向量[R6,R8],阐明了LQ分解在子空间方法中的作用.利用联合输入输出法[J1]和两阶段法[J5,R1],分别推导了基于正交分解的子空间辨识算法。并将两阶段法应用于工业过程辨识[R5].利用Hilbert空间中的LQ分解,导出了一种新的随机实现算法[J3],并成功地应用于闭环随机实现[R7]。此外,还分析了有限区间随机平衡实现算法,并证明了在有限协方差序列存在的假设下,有限区间平衡实现算法能提供稳定的最小相位模型[J 4,R2].利用J-谱分解和H-无穷辨识技术[R4,R9],给出了含变量误差模型的辨识方法。此外,为了科普观测数据中的异常值,本文还将EM算法与ORT方法相结合,提出了新的识别算法[J2,R3]. Springer-Verlag出版了一本关于系统辨识的子空间方法的英文专著,它适合于研究生和应用科学家的教科书[B1]。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Role of LQ Decomposition in Subspace Identification Methods
- DOI:10.1007/978-3-540-73570-0_17
- 发表时间:2007
- 期刊:
- 影响因子:0
- 作者:T. Katayama
- 通讯作者:T. Katayama
An approach to closed-loop subspace identification by orthogonal decomposition
- DOI:10.1016/j.automatica.2007.02.011
- 发表时间:2007-09
- 期刊:
- 影响因子:0
- 作者:T. Katayama;Hideyuki Tanaka
- 通讯作者:T. Katayama;Hideyuki Tanaka
A Stochastic Realization Algorithm via Block LQ Decomposition in Hibert Space
Hibert空间中块LQ分解的随机实现算法
- DOI:
- 发表时间:2006
- 期刊:
- 影响因子:0
- 作者:Yu;Takigawa;Seiji;Hotta;Senya;Kiyasu;Sueharu;Miyahara;H.Tanaka;T.Katayama;H. Tanaka;T. Katayama;畑中 健志;H.Tanaka;T.Katayama;H. Tanaka;J.ALMutawa;H.Tanaka
- 通讯作者:H.Tanaka
Approach to Closed-loop Subspace Identification by Orthogonal Decomposition
正交分解闭环子空间辨识方法
- DOI:
- 发表时间:2007
- 期刊:
- 影响因子:0
- 作者:Yu;Takigawa;Seiji;Hotta;Senya;Kiyasu;Sueharu;Miyahara;H.Tanaka;T.Katayama
- 通讯作者:T.Katayama
EM algorithm for state-space models with observation outliers - An initialization by subspace methods
具有观测异常值的状态空间模型的 EM 算法 - 通过子空间方法进行初始化
- DOI:
- 发表时间:2005
- 期刊:
- 影响因子:0
- 作者:Yu;Takigawa;Seiji;Hotta;Senya;Kiyasu;Sueharu;Miyahara;H.Tanaka;T.Katayama;H. Tanaka;T. Katayama;畑中 健志;H.Tanaka;T.Katayama;H. Tanaka;J.ALMutawa;H.Tanaka;T.Katayama;H.Tanaka;H.Tanaka;T.Katayama;T. Katayama;T. Katayama;J. ALMuatawa;T.Katayama;Jaafar ALMutawa
- 通讯作者:Jaafar ALMutawa
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KATAYAMA Tohru其他文献
KATAYAMA Tohru的其他文献
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{{ truncateString('KATAYAMA Tohru', 18)}}的其他基金
A Unified Approach to Nonlinear Filtering by Statistical Equivalent Linearization
统计等效线性化非线性滤波的统一方法
- 批准号:
24656264 - 财政年份:2012
- 资助金额:
$ 2.27万 - 项目类别:
Grant-in-Aid for Challenging Exploratory Research
Identification of Feedback and Nonlinear Systems by using Subspace Methods
使用子空间方法识别反馈和非线性系统
- 批准号:
20560428 - 财政年份:2008
- 资助金额:
$ 2.27万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Stochastic Subspace Identification Method and its Applications to Closed-Loop Identification
随机子空间辨识方法及其在闭环辨识中的应用
- 批准号:
15560376 - 财政年份:2003
- 资助金额:
$ 2.27万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Identification of Feedback Control Systems by Subspace Methods
子空间方法辨识反馈控制系统
- 批准号:
13650485 - 财政年份:2001
- 资助金额:
$ 2.27万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Realization and Subspace Identification of Continuous-Time Stochastic Systems
连续时间随机系统的实现与子空间辨识
- 批准号:
11650446 - 财政年份:1999
- 资助金额:
$ 2.27万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Realization of Stochastic Systems with Application to Subspace Identification Method
随机系统的子空间辨识方法的实现
- 批准号:
08650488 - 财政年份:1996
- 资助金额:
$ 2.27万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Study on H_2/H_* Optimal Control Using Descriptor Riccati Equation
基于描述符Riccati方程的H_2/H_*最优控制研究
- 批准号:
04650372 - 财政年份:1992
- 资助金额:
$ 2.27万 - 项目类别:
Grant-in-Aid for General Scientific Research (C)
A Study on Design of Process Control System in the Presence of Load Change
负荷变化时过程控制系统的设计研究
- 批准号:
02650303 - 财政年份:1990
- 资助金额:
$ 2.27万 - 项目类别:
Grant-in-Aid for General Scientific Research (C)
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