{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T02:00:09Z","timestamp":1781316009327,"version":"3.54.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>Multistep prediction models are essential for the simulation and model-predictive control of dynamical systems. Verifying the safety of such models is a multi-faceted problem requiring both system-theoretic guarantees as well as establishing trust with human users. In this work, we propose a novel approach, ReLiNet (Recurrent Linear Parameter Varying Network), to ensure safety for multistep prediction of dynamical systems. Our approach simplifies a recurrent neural network to a switched linear system that is constrained to guarantee exponential stability, which acts as a surrogate for safety from a system-theoretic perspective. Furthermore, ReLiNet's computation can be reduced to a single linear model for each time step, resulting in predictions that are explainable by definition, thereby establishing trust from a human-centric perspective. Our quantitative experiments show that ReLiNet achieves prediction accuracy comparable to that of state-of-the-art recurrent neural networks, while achieving more faithful and robust explanations compared to the model-agnostic explanation method of LIME.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/385","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"3461-3469","source":"Crossref","is-referenced-by-count":5,"title":["ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks"],"prefix":"10.24963","author":[{"given":"Alexandra","family":"Baier","sequence":"first","affiliation":[{"name":"Analytic Computing, University of Stuttgart, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Decky","family":"Aspandi","sequence":"additional","affiliation":[{"name":"Analytic Computing, University of Stuttgart, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steffen","family":"Staab","sequence":"additional","affiliation":[{"name":"Analytic Computing, University of Stuttgart, Germany"},{"name":"Web and Internet Science Research Group, University of Southampton, Great Britain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","theme":"Artificial Intelligence","location":"Macau, SAR China","acronym":"IJCAI-2023","number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2023,8,19]]},"end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:47:28Z","timestamp":1691743648000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/385"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/385","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}