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Conference Paper: A tensor-based Volterra series black-box nonlinear system identification and simulation framework

TitleA tensor-based Volterra series black-box nonlinear system identification and simulation framework
Authors
KeywordsBlack box
Volterra series
Nonlinear system identification
Tensors simulation
Issue Date2016
PublisherAssociation for Computing Machinery Inc.
Citation
Proceedings of the 35th International Conference on Computer-Aided Design (ICCAD '16 ), Austin, TX., 7-10 November 2016, p. Article No. 17 How to Cite?
AbstractTensors are a multi-linear generalization of matrices to their d-way counterparts, and are receiving intense interest recently due totheir natural representation of high-dimensional data and the availability of fast tensor decomposition algorithms. Given the inputoutput data of a nonlinear system/circuit, this paper presents a nonlinear model identification and simulation framework built on top of Volterra series and its seamless integration with tensor arithmetic. By exploiting partially-symmetric polyadic decompositions of sparse Toeplitz tensors, the proposed framework permits a pleasantly scalable way to incorporate high-order Volterra kernels. Such an approach largely eludes the curse of dimensionality and allows computationally fast modeling and simulation beyond weakly nonlinear systems. The black-box nature of the model also hides structural information of the system/circuit and encapsulates it in terms of compact tensors. Numerical examples are given to verify the efficacy, efficiency and generality of this tensor-based modeling and simulation framework.
Persistent Identifierhttp://hdl.handle.net/10722/229783
ISBN

 

DC FieldValueLanguage
dc.contributor.authorBatselier, K-
dc.contributor.authorChen, Z-
dc.contributor.authorLiu, H-
dc.contributor.authorWong, N-
dc.date.accessioned2016-08-23T14:13:15Z-
dc.date.available2016-08-23T14:13:15Z-
dc.date.issued2016-
dc.identifier.citationProceedings of the 35th International Conference on Computer-Aided Design (ICCAD '16 ), Austin, TX., 7-10 November 2016, p. Article No. 17-
dc.identifier.isbn978-1-4503-4466-1-
dc.identifier.urihttp://hdl.handle.net/10722/229783-
dc.description.abstractTensors are a multi-linear generalization of matrices to their d-way counterparts, and are receiving intense interest recently due totheir natural representation of high-dimensional data and the availability of fast tensor decomposition algorithms. Given the inputoutput data of a nonlinear system/circuit, this paper presents a nonlinear model identification and simulation framework built on top of Volterra series and its seamless integration with tensor arithmetic. By exploiting partially-symmetric polyadic decompositions of sparse Toeplitz tensors, the proposed framework permits a pleasantly scalable way to incorporate high-order Volterra kernels. Such an approach largely eludes the curse of dimensionality and allows computationally fast modeling and simulation beyond weakly nonlinear systems. The black-box nature of the model also hides structural information of the system/circuit and encapsulates it in terms of compact tensors. Numerical examples are given to verify the efficacy, efficiency and generality of this tensor-based modeling and simulation framework.-
dc.languageeng-
dc.publisherAssociation for Computing Machinery Inc.-
dc.relation.ispartofInternational Conference on Computer Aided Design, ICCAD 2016-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectBlack box-
dc.subjectVolterra series-
dc.subjectNonlinear system identification-
dc.subjectTensors simulation-
dc.titleA tensor-based Volterra series black-box nonlinear system identification and simulation framework-
dc.typeConference_Paper-
dc.identifier.emailBatselier, K: kbatseli@hku.hk-
dc.identifier.emailChen, Z: zmchen@hku.hk-
dc.identifier.emailLiu, H: htliu@eee.hku.hk-
dc.identifier.emailWong, N: nwong@eee.hku.hk-
dc.identifier.authorityWong, N=rp00190-
dc.description.naturepostprint-
dc.identifier.doi10.1145/2966986.2966996-
dc.identifier.hkuros260795-
dc.identifier.hkuros274507-
dc.identifier.spageArticle No. 17-
dc.identifier.epageArticle No. 17-
dc.publisher.placeNew York-
dc.customcontrol.immutablesml 160905-

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