| 基于神经网络的切换非线性系统辨识 |
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| 引用本文： | 王林,王宏伟,柴秀俊. 基于神经网络的切换非线性系统辨识[J]. 科学技术与工程, 2021, 21(5): 1914-1921 |
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| 作者姓名： | 王林 王宏伟 柴秀俊 |
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| 作者单位： | 新疆大学电气工程学院,乌鲁木齐830047;新疆大学电气工程学院,乌鲁木齐830047;大连理工大学控制科学与工程学院,大连116024 |
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| 基金项目： | 国家自然科学基金项目（面上项目,重点项目,重大项目） |
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| 摘 要： | 提出了一种基于神经网络的多个Hammerstein-Wiener模型构成切换非线性系统的在线辨识方法.首先,通过误差逆传播(back propagation,BP)神经网络建立切换非线性系统的切换规律预测模型;其次,提出折息递推辨识算法对各个非线性子系统的参数进行辨识.利用关键项分离法对乘积项进行分离,得到各个子系统的...
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| 关 键 词： | 切换非线性系统 Hammerstein-Wiener模型 BP神经网络 折息递推辨识算法 关键项分离法 |
| 收稿时间： | 2020-05-06 |
| 修稿时间： | 2020-11-23 |
Identification of switched nonlinear systems based on neural networks |
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| Wang Lin,Wang Hongwei,ChaiI Xiujun. Identification of switched nonlinear systems based on neural networks[J]. Science Technology and Engineering, 2021, 21(5): 1914-1921 |
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| Authors: | Wang Lin Wang Hongwei ChaiI Xiujun |
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| Affiliation: | Department of School of electrical engineering,Xinjiang University,Urumqi,,Department of School of electrical engineering,Xinjiang University,Urumqi |
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| Abstract: | An on-line identification method for switching nonlinear systems with multiple Hammerstein-Wiener models based on neural networks is proposed. First of all, a switching rule prediction model for switching non-linear systems is established through a BP neural network; Secondly, the recursive method with discounted measurements is proposed to identify the parameters of each nonlinear subsystem. The key term separation method is used to separate the product terms, and the parameter estimates of each subsystem are obtained. Finally, an example of nonlinear system identification is simulated and compared with other methods to verify the effectiveness of the proposed method. |
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| Keywords: | switching nonlinear systems hammerstein-wiener models bp neural network recursive method with discounted measurements key-term separation method |
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