| 输入非线性方程误差自回归系统的多新息辨识方法 |
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| 引用本文： | 丁锋,毛亚文.输入非线性方程误差自回归系统的多新息辨识方法[J].南京气象学院学报,2015,7(1):1-23. |
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| 作者姓名： | 丁锋 毛亚文 |
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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 |
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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);高等学校学科创新引智"111计划"(B12018) |
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| 摘 要： | 典型块结构非线性系统包括基本的输入非线性系统、输出非线性系统、输入输出非线性系统、反馈非线性系统等.输入非线性系统包括输入非线性方程误差类系统和输入非线性输出误差类系统.以输入非线性方程误差自回归系统,即输入非线性受控自回归自回归(IN-CARAR)系统为例,分别基于过参数化模型,基于关键项分离原理,基于数据滤波技术以及基于辨识模型分解技术,研究和提出了IN-CARAR系统的随机梯度辨识方法、多新息随机梯度辨识方法、递推最小二乘辨识方法、多新息最小二乘辨识方法.这些方法可以推广到其他输入非线性方程误差系统、输入非线性输出误差类系统、输出非线性方程误差类系统、输出非线性输出类系统、反馈非线性系统等.同时,给出了几个典型辨识算法的计算步骤、流程图和计算量.
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| 关 键 词： | 参数估计 递推辨识 梯度搜索 最小二乘 过参数化模型 关键项分离原理 数据滤波技术 模型分解 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 输入非线性系统 输出非线性系统 |
| 收稿时间： | 2015/1/25 0:00:00 |
Multi-innovation identification methods for input nonlinear equation-error autoregressive systems |
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| DING Feng and MAO Yawen.Multi-innovation identification methods for input nonlinear equation-error autoregressive systems[J].Journal of Nanjing Institute of Meteorology,2015,7(1):1-23. |
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| Authors: | DING Feng and MAO Yawen |
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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 |
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| Abstract: | Typical block-oriented structure nonlinear systems include the basic input nonlinear systems,the output nonlinear systems,the input-output nonlinear systems and the feedback nonlinear systems.The input nonlinear systems include the input nonlinear equation-error type systems and the input nonlinear output-error type systems.Taking the input nonlinear equation-error autoregressive systems (namely the input nonlinear controlled autoregressive autoregressive (IN-CARAR) systems as an example,this paper studies and presents stochastic gradient (SG) identification methods,multi-innovation SG methods,recursive least squares (LS) identification methods and multi-innovation LS identification methods for IN-CARAR systems based on the over-parameterization model,the key term separation principle and the data filtering technique,the model decomposition technique.These methods can be extended to other input nonlinear equation-error systems,input nonlinear output-error type systems,output nonlinear equation-error type systems and output nonlinear output-error systems,and feedback nonlinear systems.Finally,the computational efficiency,the computational steps and the flowcharts of several typical identification algorithms are discussed. |
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| Keywords: | parameter estimation recursive identification gradient search least squares over-parameterization model key term separation principle data filtering technique model decomposition etchnique auxiliary model identification ideal multi-innovation identification theory hierarchical identification principle coupling identification concept input nonlinear system output nonlinear system |
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