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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2505.17696 |
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| _version_ | 1866911091293945856 |
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| author | Yoshihara, Sota Yamamoto, Ryosuke Kusumoto, Hiroyuki Shimura, Masanari |
| author_facet | Yoshihara, Sota Yamamoto, Ryosuke Kusumoto, Hiroyuki Shimura, Masanari |
| contents | This paper proposes a novel theoretical framework for guaranteeing and evaluating the resilience of long short-term memory (LSTM) networks in control systems. We introduce "recovery time" as a new metric of resilience in order to quantify the time required for an LSTM to return to its normal state after anomalous inputs. By mathematically refining incremental input-to-state stability ($δ$ISS) theory for LSTM, we derive a practical data-independent upper bound on recovery time. This upper bound gives us resilience-aware training. Experimental validation on simple models demonstrates the effectiveness of our resilience estimation and control methods, enhancing a foundation for rigorous quality assurance in safety-critical AI applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17696 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing AI System Resiliency: Formulation and Guarantee for LSTM Resilience Based on Control Theory Yoshihara, Sota Yamamoto, Ryosuke Kusumoto, Hiroyuki Shimura, Masanari Artificial Intelligence Systems and Control This paper proposes a novel theoretical framework for guaranteeing and evaluating the resilience of long short-term memory (LSTM) networks in control systems. We introduce "recovery time" as a new metric of resilience in order to quantify the time required for an LSTM to return to its normal state after anomalous inputs. By mathematically refining incremental input-to-state stability ($δ$ISS) theory for LSTM, we derive a practical data-independent upper bound on recovery time. This upper bound gives us resilience-aware training. Experimental validation on simple models demonstrates the effectiveness of our resilience estimation and control methods, enhancing a foundation for rigorous quality assurance in safety-critical AI applications. |
| title | Enhancing AI System Resiliency: Formulation and Guarantee for LSTM Resilience Based on Control Theory |
| topic | Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2505.17696 |