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Identification and Structure Selection for Nonlinear Stochastic Systems

Identification and Structure Selection for Nonlinear Stochastic Systems *
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摘要 A nonlinear model is proposed for effective adaptive control design. The model represents a natural way to describe input output nonlinear systems. A combined parameter off line estimation and structure detection algorithm is developed that can use an initial set of data. Then, an efficient model is obtained using orthogonal estimation with an error reduction test and other monitoring modifications. A recursive on line identification scheme is established based on the ELS algorithm to account for future time variations in the process of the parsimonious model. A nonlinear model is proposed for effective adaptive control design. The model represents a natural way to describe input output nonlinear systems. A combined parameter off line estimation and structure detection algorithm is developed that can use an initial set of data. Then, an efficient model is obtained using orthogonal estimation with an error reduction test and other monitoring modifications. A recursive on line identification scheme is established based on the ELS algorithm to account for future time variations in the process of the parsimonious model.
作者 罗贵明
出处 《Tsinghua Science and Technology》 SCIE EI CAS 1997年第3期92-95,共4页 清华大学学报(自然科学版(英文版)
关键词 nonlinear systems structure selection parameter estimation error reduction test ELS algorithm nonlinear systems structure selection parameter estimation error reduction test ELS algorithm
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