| 类多变量输出误差系统的耦合多新息辨识方法 |
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| 引用本文： | 丁锋,汪菲菲,汪学海.类多变量输出误差系统的耦合多新息辨识方法[J].南京气象学院学报,2014,6(3):193-210. |
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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： | 辅助模型辨识思想、多新息辨识理论、耦合辨识概念是研究复杂多变量系统辨识的新理念和原理.将它们结合起来研究类多变量输出误差系统的辨识问题，提出了多元辅助模型辨识方法、多元辅助模型多新息辨识方法、变递推间隔多元辅助模型多新息辨识方法.为减小算法的计算量和提高参数估计精度，将系统模型分解为一些子辨识模型，应用辅助模型辨识思想、多新息辨识理论、耦合辨识概念，研究和推导了部分耦合辅助模型辨识方法、部分耦合辅助模型多新息辨识方法.讨论了几个典型辨识算法的计算量，给出了参数估计的计算步骤和计算流程图.
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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 类多变量系统 |
| 收稿时间： | 2014/6/7 0:00:00 |
Coupled multi-innovation identification methods for multivariable output-error-like systems |
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| DING Feng,WANG Feifei and WANG Xuehai.Coupled multi-innovation identification methods for multivariable output-error-like systems[J].Journal of Nanjing Institute of Meteorology,2014,6(3):193-210. |
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| Authors: | DING Feng WANG Feifei and WANG Xuehai |
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| Affiliation: | 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: | The auxiliary model identification idea,the multi-innovation identification theory and the coupling identification concept are the new ideas and principles for studying identification problems of complex systems.Combining them,this paper studies the identification methods of multivariable output-error-like systems and presents multivariate auxiliary model identification methods,multivariate auxiliary model based multi-innovation identification methods,interval-varying multivariate auxiliary model based multi-innovation identification methods.In order to reduce the computational complexity of the algorithms,we decompose the system into several sub-identification models and derive the partially coupled auxiliary model based identification methods and the partially coupled auxiliary model based multi-innovation identification methods,using the auxiliary model identification idea,the multi-innovation identification theory and the coupling identification concept.Finally,the computational efficiency,the computational steps and the flowcharts of some typical identification algorithms are discussed. |
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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 multivariable-like system |
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