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作 者:侯玉雯 王宏伟 HOU Yu-wen;WANG Hong-wei(Department of School of Electrical Engineering,Xinjiang University,Urumqi,830047,China;School of Control Science and Engineering,Dalian University of Technology,Dalian 116024,China)
机构地区:[1]新疆大学电气工程学院,乌鲁木齐830047 [2]大连理工大学控制科学与工程学院,大连116024
出 处:《科学技术与工程》2021年第10期4084-4091,共8页Science Technology and Engineering
基 金:国家自然科学基金(61863034)。
摘 要:为解决对非线性采样系统的状态空间Hammerstein模型难以辨识的问题,提出了基于组合信号源的辨识方法。首先用组合信号源将静态非线性环节和动态线性环节分离。其次,采用模糊神经模型拟合静态非线性环节,有效地避免了采用多项式方法逼近非线性函数的限制,拓宽了非线性模型的适用范围;采用子空间算法估计采样系统的状态空间参数矩阵。最后,通过对两个非线性Hammerstein系统模型的仿真,验证了所提出的辨识方法,既简化了辨识过程,对非线性模块能够较好地拟合,又可以很快估计出状态空间方程系数矩阵,从而证明了所提方法的准确性和有效性。An identification method based on combined signal sources was proposed to solve the difficult identification problem of state space Hammerstein model for nonlinear sampling systems. Firstly, the static nonlinear part and the dynamic linear part were separated by the hybrid signal source. Secondly, the neural fuzzy model was used to approach to the static nonlinear part, which effectively avoided the limitation of approaching the nonlinear function by polynomial method and broadened the applicable scope of the nonlinear model. In addition, the subspace algorithm was used to estimate the state space parameter matrix of the sampling system. Finally, through the simulation of two nonlinear Hammerstein system models, the proposed identification method is verified, which not only simplifies the identification process, fits the nonlinear module well, but also can quickly estimate the state space equation coefficient matrix, which proves the accuracy and effectiveness of the proposed method.
关 键 词:组合信号源 子空间辨识方法 模糊神经网络 系统辨识
分 类 号:TN911.7[电子电信—通信与信息系统]
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