A tensor-based volterra series black-box nonlinear system identification and simulation framework

Tensors are a multi-linear generalization of matrices to their d-way counterparts, and are receiving intense interest recently due to their natural representation of high-dimensional data and the availability of fast tensor decomposition algorithms. Given the input-output data of a nonlinear system/...

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Veröffentlicht in:Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design S. 1 - 7
Hauptverfasser: Batselier, Kim, Zhongming Chen, Haotian Liu, Ngai Wong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 01.11.2016
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ISSN:1558-2434
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Abstract Tensors are a multi-linear generalization of matrices to their d-way counterparts, and are receiving intense interest recently due to their natural representation of high-dimensional data and the availability of fast tensor decomposition algorithms. Given the input-output data of a nonlinear system/circuit, this paper presents a non-linear 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 non-linear 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.
AbstractList Tensors are a multi-linear generalization of matrices to their d-way counterparts, and are receiving intense interest recently due to their natural representation of high-dimensional data and the availability of fast tensor decomposition algorithms. Given the input-output data of a nonlinear system/circuit, this paper presents a non-linear 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 non-linear 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.
Author Ngai Wong
Zhongming Chen
Haotian Liu
Batselier, Kim
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  surname: Zhongming Chen
  fullname: Zhongming Chen
  email: zmchen@eee.hku.hk
  organization: Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China
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  surname: Haotian Liu
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  organization: Cadence Design Syst. Inc., Austin, TX, USA
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  surname: Ngai Wong
  fullname: Ngai Wong
  email: nwong@eee.hku.hk
  organization: Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China
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Snippet Tensors are a multi-linear generalization of matrices to their d-way counterparts, and are receiving intense interest recently due to their natural...
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SubjectTerms black box
Computational modeling
Convolution
Kernel
Linear systems
nonlinear system identification
Nonlinear systems
Numerical models
simulation
Tensile stress
tensors
Volterra series
Title A tensor-based volterra series black-box nonlinear system identification and simulation framework
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