| 摘 要: 传统的Wiener系统在工业系统建模方面获得了大量应用，但是当系统含有动态非线性环节时，就会因为 模型不匹配导致建模效果不佳。为了更好的对这类系统进行建模，必须将传统Wiener系统中的静态非线性模块扩展为 动态非线性形式。在采用全新结构的基础上，基于关键项分离技术参数化系统以减小算法计算量，并避免出现参数乘 积项；对数据进行滤波以获得参数的无偏估计；运用最小二乘算法以获得健壮的参数估计值。数值仿真表明了算法的 有效性。 |
| 关键词: 参数辨识；广义Wiener系统；关键项分离；最小二乘算法 |
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中图分类号: TP391.9
文献标识码: A
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| 基金项目: 本文系淮阴师范学院大学生创新创业基金资助(项目编号：201817008XJ). |
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| Parameter Identification Algorithm for a Class of Generalized Wiener Systems |
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JING Shaoxue,FAN Mengsong,LI Dongmei
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( School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huai'an 223300, China)
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| Abstract: The traditional Wiener system has been widely applied in the modeling of industrial system.However,when the system contains dynamic nonlinear elements,the model obtained is not optimal because of the non-matching model. In order to model this kind of system better,the static nonlinear block in the traditional Wiener system must be extended to dynamic nonlinear form.Firstly,the original system with new structure is parameterized based on key-term separation technology to reduce the computation,and to avoid the product terms of the parameters;secondly,the input and output data are filtered to estimate the known parameters without bias;At last,the least squares algorithm is used to obtain robust parameter estimates.Numerical simulation shows the effectiveness of the algorithm. |
| Keywords: parameter identification;generalized Wiener systems;key-term separation;least squares algorithm |
