Stability verification in stochastic control systems via neural network supermartingales
We consider the problem of formally verifying almost-sure (a.s.) asymptotic stability in discrete-time nonlinear stochastic control systems. While verifying stability in deterministic control systems is extensively studied in the literature, verifying stability in stochastic control systems is an op...
محفوظ في:
المؤلفون الرئيسيون: | LECHNER, Mathias, ZIKELIC, Dorde, CHATTERJEE, Krishnendu, HENZINGER, Thomas A. |
---|---|
التنسيق: | text |
اللغة: | English |
منشور في: |
Institutional Knowledge at Singapore Management University
2024
|
الموضوعات: | |
الوصول للمادة أونلاين: | https://ink.library.smu.edu.sg/sis_research/9077 https://ink.library.smu.edu.sg/context/sis_research/article/10080/viewcontent/20695_13_24708_1_2_20220628__1_.pdf |
الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
مواد مشابهة
-
Infinite time horizon safety of Bayesian neural networks
بواسطة: LECHNER, Mathias, وآخرون
منشور في: (2021) -
Scalable verification of quantized neural networks
بواسطة: HENZINGER, Thomas A., وآخرون
منشور في: (2021) -
Quantization-aware interval bound propagation for training certifiably robust quantized neural networks
بواسطة: LECHNER, Mathias, وآخرون
منشور في: (2023) -
A learner-verifier framework for neural network controllers and certificates of stochastic systems
بواسطة: CHATTERJEE, Krishnendu, وآخرون
منشور في: (2023) -
Learning provably stabilizing neural controllers for discrete-time stochastic systems
بواسطة: ANSARIPOUR, Matin, وآخرون
منشور في: (2023)