# Deep Convolutional Networks in System Identification

arXiv: https://arxiv.org/abs/1909.01730 · HTML: https://mlsanity.com/paper/1909.01730/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2019.

## Built on by (most cited first, 17 of 17)

- [Deep networks for system identification: a Survey](https://mlsanity.com/paper/2301.12832/) (2023)
- [From system models to class models: An in-context learning paradigm](https://mlsanity.com/paper/2308.13380/) (2023)
- [On the adaptation of recurrent neural networks for system identification](https://mlsanity.com/paper/2201.08660/) (2022)
- [RIANN -- A Robust Neural Network Outperforms Attitude Estimation Filters](https://mlsanity.com/paper/2104.07391/) (2021)
- [Adaptive Attitude Estimation Using a Hybrid Model-Learning Approach](https://mlsanity.com/paper/2207.06903/) (2022)
- [Structured state-space models are deep Wiener models](https://mlsanity.com/paper/2312.06211/) (2023)
- [Deep learning with transfer functions: new applications in system identification](https://mlsanity.com/paper/2104.09839/) (2021)
- [Non-Autoregressive vs Autoregressive Neural Networks for System Identification](https://mlsanity.com/paper/2105.02027/) (2021)
- [Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification](https://mlsanity.com/paper/2304.06349/) (2023)
- [L2RU: a Structured State Space Model with prescribed L2-bound](https://mlsanity.com/paper/2503.23818/) (2025)
- [Deep Learning Based Model Identification System Exploits the Modular Structure of a Bio-Inspired Posture Control Model for Humans and Humanoids](https://mlsanity.com/paper/2102.02536/) (2021)
- [Dynamical System Parameter Identification using Deep Recurrent Cell Networks](https://mlsanity.com/paper/2107.02427/) (2021)
- [Orthogonal Transforms in Neural Networks Amount to Effective Regularization](https://mlsanity.com/paper/2305.06344/) (2023)
- [Exploiting the capacity of deep networks only at training stage for nonlinear black-box system identification](https://mlsanity.com/paper/2312.15969/) (2023)
- [A neural network-based approach to hybrid systems identification for control](https://mlsanity.com/paper/2404.01814/) (2024)
- [S-Crescendo: A Nested Transformer Weaving Framework for Scalable Nonlinear System in S-Domain Representation](https://mlsanity.com/paper/2505.11843/) (2025)
- [Causal Convolutional Neural Networks as Finite Impulse Response Filters](https://mlsanity.com/paper/2510.24125/) (2025)

## Abstract (arXiv)

Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures when it comes to sequence modeling tasks. Inspired by these results we explore the explicit relationships between the recently proposed temporal convolutional network (TCN) and two classic system identification model structures; Volterra series and block-oriented models. We end the paper with an experimental study where we provide results on two real-world problems, the well-known Silverbox dataset and a newer dataset originating from ground vibration experiments on an F-16 fighter aircraft.