Modeling nonlinear systems using the tensor network B‐spline and the multi‐innovation identification theory

Yanjiao Wang, Shihua Tang, Muqing Deng · 2022 · International Journal of Robust and Nonlinear Control

Summary. This paper develops a tensor network B-spline method to model nonlinear autoregressive exogenous systems with high dimensions. The approach uses multi-innovation identification theory and hierarchical principles to create a recursive algorithm that handles Gaussian noise. The method outperforms traditional polynomial and neural network approaches by reducing computational burden while maintaining strong fitting capacity for highly nonlinear systems.

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@article{wang-2022-modeling-nonlinear-systems-using-tensor,
  title   = {Modeling nonlinear systems using the tensor network B‐spline and the multi‐innovation identification theory},
  author  = {Yanjiao Wang and Shihua Tang and Muqing Deng},
  journal = {International Journal of Robust and Nonlinear Control},
  year    = {2022},
  doi     = {10.1002/rnc.6221},
  url     = {https://doi.org/10.1002/rnc.6221}
}

TY  - JOUR
TI  - Modeling nonlinear systems using the tensor network B‐spline and the multi‐innovation identification theory
AU  - Yanjiao Wang
AU  - Shihua Tang
AU  - Muqing Deng
JO  - International Journal of Robust and Nonlinear Control
PY  - 2022
DO  - 10.1002/rnc.6221
UR  - https://doi.org/10.1002/rnc.6221
ER  - 

Details

DOI

10.1002/rnc.6221

Countries

China

Regions

Asia

Categories

innovation-theory, general-innovation

Added

2026-04-28