| 辅助模型辨识方法(3):输入非线性输出误差自回归系统 |
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| 引用本文： | 丁锋,毛亚文. 辅助模型辨识方法(3):输入非线性输出误差自回归系统[J]. 南京信息工程大学学报, 2016, 8(3): 193-214 |
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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： | 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.针对输入非线性输出误差自回归系统,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,研究了相应的基于过参数化模型的辅助模型递推辨识方法、基于关键项分离的辅助模型递推辨识方法、基于数据滤波的辅助模型递推辨识方法.这些方法可以推广到其他输入非线性输出误差系统、输出非线性输出误差系统、反馈非线性系统等.并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 滤波技术 模型分解 辅助模型辨识思想 递阶辨识原理 输入非线性系统 输出非线性系统 |
| 收稿时间： | 2016-03-30 |
Auxiliary model based identification methods.Part C: Input nonlinear output-error autoregressive systems |
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| DING Feng and MAO Yawen. Auxiliary model based identification methods.Part C: Input nonlinear output-error autoregressive systems[J]. Journal of Nanjing University of Information Science & Technology, 2016, 8(3): 193-214 |
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| Authors: | DING Feng and MAO Yawen |
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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 |
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| Abstract: | The input nonlinear systems include the input nonlinear equation-error type systems and the input nonlinear output-error type systems.According to the over-parameterization model,the key term separation and the data filtering,this paper studies and presents the over-parameterization model based auxiliary model recursive identification (AM-RI) methods,the key term separation based AM-RI methods and the data filtering based AM-RI methods for input nonlinear output-error autoregressive systems.These methods can be extended to other input nonlinear output-error systems,output nonlinear output-error type 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 filtering technique model decomposition auxiliary model identification ideal hierarchical identification principle input nonlinear system output nonlinear system |
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