# Real-time Hybrid System Identification with Online Deterministic Annealing

arXiv: https://arxiv.org/abs/2408.01730 · HTML: https://mlsanity.com/paper/2408.01730/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 3 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [Continuation Path Learning for Homotopy Optimization](https://mlsanity.com/paper/2307.12551/) (2023)
- [Fitting Jump Models](https://mlsanity.com/paper/1711.09220/) (2017)
- [Online Deterministic Annealing for Classification and Clustering](https://mlsanity.com/paper/2102.05836/) (2021)
- [Convergence Properties of Adaptive Systems and the Definition of Exponential Stability](https://mlsanity.com/paper/1511.03222/) (2015)
- [Annealing Optimization for Progressive Learning with Stochastic Approximation](https://mlsanity.com/paper/2209.02826/) (2022)
- [Multi-Resolution Online Deterministic Annealing: A Hierarchical and Progressive Learning Architecture](https://mlsanity.com/paper/2212.08189/) (2022)

## Abstract (arXiv)

We introduce a real-time identification method for discrete-time state-dependent switching systems in both the input--output and state-space domains. In particular, we design a system of adaptive algorithms running in two timescales; a stochastic approximation algorithm implements an online deterministic annealing scheme at a slow timescale and estimates the mode-switching signal, and an recursive identification algorithm runs at a faster timescale and updates the parameters of the local models based on the estimate of the switching signal. We first focus on piece-wise affine systems and discuss identifiability conditions and convergence properties based on the theory of two-timescale stochastic approximation. In contrast to standard identification algorithms for switched systems, the proposed approach gradually estimates the number of modes and is appropriate for real-time system identification using sequential data acquisition. The progressive nature of the algorithm improves computational efficiency and provides real-time control over the performance-complexity trade-off. Finally, we address specific challenges that arise in the application of the proposed methodology in identification of more general switching systems. Simulation results validate the efficacy of the proposed methodology.