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An Online Active Broad Learning Approach for Real-Time Safety Assessment of Dynamic Systems in Nonstationary Environments.
IEEE Transactions on Neural Networks and Learning Systems ( IF 10.4 ) Pub Date : 2023-10-06 , DOI: 10.1109/tnnls.2022.3222265 Zeyi Liu 1 , Yi Zhang 2 , Zhongjun Ding 3 , Xiao He 4
IEEE Transactions on Neural Networks and Learning Systems ( IF 10.4 ) Pub Date : 2023-10-06 , DOI: 10.1109/tnnls.2022.3222265 Zeyi Liu 1 , Yi Zhang 2 , Zhongjun Ding 3 , Xiao He 4
Affiliation
Real-time safety assessment of the complex dynamic systems in nonstationary environments is of great significance for avoiding the potential hazards. In this case, the update procedure with high assessment accuracy and training speed is crucial and meaningful in the dynamic streaming setting. Generally, the performance of most online learning approaches will be negatively affected by limited annotated samples in such a setting. Moreover, the time cost of advanced conventional methods with retaining procedures is relatively high, constraining their practicality. In this article, a novel online active broad learning approach, termed OABL, is proposed. In detail, the effectiveness of the broad learning system in the framework of online active learning is first revealed and verified. A reasonable dynamic asymmetric query strategy is then designed with a limited annotation budget to actively annotate the relatively valuable samples, which is beneficial to mitigating the negative effects of class imbalance. In this context, the advantage of the human-in-the-loop characteristic is also effectively used to control the evolution direction of the learner during the incremental update, which makes it better able to adapt to complex and nonstationary environments. Several related experiments are conducted with the realistic data of JiaoLong deep-sea manned submersible. Results show the effectiveness and practicality of the proposal compared with the existing advanced approaches.
中文翻译:
用于非平稳环境中动态系统实时安全评估的在线主动广泛学习方法。
非平稳环境下复杂动态系统的实时安全评估对于避免潜在危险具有重要意义。在这种情况下,具有高评估精度和训练速度的更新过程在动态流设置中至关重要且有意义。一般来说,在这种情况下,大多数在线学习方法的性能都会受到有限注释样本的负面影响。此外,先进的保留程序传统方法的时间成本相对较高,限制了其实用性。在本文中,提出了一种新颖的在线主动广泛学习方法,称为 OABL。具体而言,在线主动学习框架下的广泛学习系统的有效性首次得到揭示和验证。然后在有限的标注预算下设计合理的动态非对称查询策略,主动标注相对有价值的样本,这有利于减轻类别不平衡的负面影响。在此背景下,人在环特性的优势也被有效地利用,在增量更新过程中控制学习器的进化方向,使其能够更好地适应复杂和非平稳的环境。利用蛟龙号深海载人潜水器的真实数据进行了多项相关实验。结果表明,与现有的先进方法相比,该提案的有效性和实用性。
更新日期:2022-11-23
中文翻译:
用于非平稳环境中动态系统实时安全评估的在线主动广泛学习方法。
非平稳环境下复杂动态系统的实时安全评估对于避免潜在危险具有重要意义。在这种情况下,具有高评估精度和训练速度的更新过程在动态流设置中至关重要且有意义。一般来说,在这种情况下,大多数在线学习方法的性能都会受到有限注释样本的负面影响。此外,先进的保留程序传统方法的时间成本相对较高,限制了其实用性。在本文中,提出了一种新颖的在线主动广泛学习方法,称为 OABL。具体而言,在线主动学习框架下的广泛学习系统的有效性首次得到揭示和验证。然后在有限的标注预算下设计合理的动态非对称查询策略,主动标注相对有价值的样本,这有利于减轻类别不平衡的负面影响。在此背景下,人在环特性的优势也被有效地利用,在增量更新过程中控制学习器的进化方向,使其能够更好地适应复杂和非平稳的环境。利用蛟龙号深海载人潜水器的真实数据进行了多项相关实验。结果表明,与现有的先进方法相比,该提案的有效性和实用性。