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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.19627 |
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| _version_ | 1866916966858489856 |
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| author | Memmel, Eva Batselier, Kim |
| author_facet | Memmel, Eva Batselier, Kim |
| contents | The Volterra Tensor Network lifts the curse of dimensionality for truncated, discrete times Volterra models, enabling scalable representation of highly nonlinear system. This scalability comes at the cost of introducing randomness through initialization, and leaves open the challenge of how to efficiently determine the hyperparameters model order and memory length. In this paper, we present a unified framework that simultaneously addresses both challenges: We derive two algorithms that incrementally increase the model order and memory length along. Further we proof that the updates are performed along conjugate directions by establishing a mathematical equivalence between our proposed algorithms and equality constrained least squares systems. We present several strategies how to use our proposed algorithms for initialization and hyperparameter selection. In numerical experiments, we demonstrate that our proposed algorithms are more accurate and efficient than the state-of-the-art Volterra Tensor Network and achieve competitive results to several state-of-the-art Volterra models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19627 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic Structure Identification for Highly Nonlinear MIMO Volterra Tensor Networks Memmel, Eva Batselier, Kim Optimization and Control The Volterra Tensor Network lifts the curse of dimensionality for truncated, discrete times Volterra models, enabling scalable representation of highly nonlinear system. This scalability comes at the cost of introducing randomness through initialization, and leaves open the challenge of how to efficiently determine the hyperparameters model order and memory length. In this paper, we present a unified framework that simultaneously addresses both challenges: We derive two algorithms that incrementally increase the model order and memory length along. Further we proof that the updates are performed along conjugate directions by establishing a mathematical equivalence between our proposed algorithms and equality constrained least squares systems. We present several strategies how to use our proposed algorithms for initialization and hyperparameter selection. In numerical experiments, we demonstrate that our proposed algorithms are more accurate and efficient than the state-of-the-art Volterra Tensor Network and achieve competitive results to several state-of-the-art Volterra models. |
| title | Automatic Structure Identification for Highly Nonlinear MIMO Volterra Tensor Networks |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2509.19627 |