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 |
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Kim surname: Batselier fullname: Batselier, Kim email: kimb@eee.hku.hk organization: Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China – sequence: 2 surname: Zhongming Chen fullname: Zhongming Chen email: zmchen@eee.hku.hk organization: Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China – sequence: 3 surname: Haotian Liu fullname: Haotian Liu email: haotian@cadence.com organization: Cadence Design Syst. Inc., Austin, TX, USA – sequence: 4 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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