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Conference Paper: Statespace adaptive filtering based balanced realization
Title  Statespace adaptive filtering based balanced realization 

Authors  
Keywords  Filtering FIR coefficients Adaptive Balanced Realization 
Issue Date  2013 
Citation  Proceedings of International Conference on Computers and Industrial Engineering, CIE, 2013, v. 1, p. 440450 How to Cite? 
Abstract  Balanced realizations are attractive candidates for statespace adaptive filter structures due to low parameter sensitivity. Since the balanced realization minimizes the ratio of maximumtominimum eigenvalues of the Grammian matrices, this property may lead to an adaptive filter exhibiting good noise rejection. Thus a balanced realization would seem an appropriate choice for the structure of an adaptive filtering algorithm. This paper focuses on answering the research question how does one use discretetime Lyapunov equations such that, upon adjusting the parameters, the terms in the system matrices vary in such a way that the solutions for the controllability and observability Grammian matrices are always diagonal and equal Here, using an alternative to the finite impulse response coefficients as the adaptive filter parameters, a statespace adaptive filtering based balanced realization is proposed for outputerror minimization. The algorithm is in internally balanced realization. Simulation results show that the balanced structure yields good noise rejection compared with the controllable canonical form in steadystate. The simulation results from the experiments imply that the approach is able to reduce the fluctuation in steadystate compared with the controllable canonical structure under the same scenarios. 
Persistent Identifier  http://hdl.handle.net/10722/222156 
DC Field  Value  Language 

dc.contributor.author  Dai, Dameng   
dc.contributor.author  Wu, Chengwen   
dc.contributor.author  Zhong, Ray Y.   
dc.date.accessioned  20151221T06:49:03Z   
dc.date.available  20151221T06:49:03Z   
dc.date.issued  2013   
dc.identifier.citation  Proceedings of International Conference on Computers and Industrial Engineering, CIE, 2013, v. 1, p. 440450   
dc.identifier.uri  http://hdl.handle.net/10722/222156   
dc.description.abstract  Balanced realizations are attractive candidates for statespace adaptive filter structures due to low parameter sensitivity. Since the balanced realization minimizes the ratio of maximumtominimum eigenvalues of the Grammian matrices, this property may lead to an adaptive filter exhibiting good noise rejection. Thus a balanced realization would seem an appropriate choice for the structure of an adaptive filtering algorithm. This paper focuses on answering the research question how does one use discretetime Lyapunov equations such that, upon adjusting the parameters, the terms in the system matrices vary in such a way that the solutions for the controllability and observability Grammian matrices are always diagonal and equal Here, using an alternative to the finite impulse response coefficients as the adaptive filter parameters, a statespace adaptive filtering based balanced realization is proposed for outputerror minimization. The algorithm is in internally balanced realization. Simulation results show that the balanced structure yields good noise rejection compared with the controllable canonical form in steadystate. The simulation results from the experiments imply that the approach is able to reduce the fluctuation in steadystate compared with the controllable canonical structure under the same scenarios.   
dc.language  eng   
dc.relation.ispartof  Proceedings of International Conference on Computers and Industrial Engineering, CIE   
dc.subject  Filtering   
dc.subject  FIR coefficients   
dc.subject  Adaptive   
dc.subject  Balanced Realization   
dc.title  Statespace adaptive filtering based balanced realization   
dc.type  Conference_Paper   
dc.description.nature  Link_to_subscribed_fulltext   
dc.identifier.scopus  eid_2s2.084898832299   
dc.identifier.volume  1   
dc.identifier.spage  440   
dc.identifier.epage  450   
dc.identifier.eissn  21648689   