神经网络动态系统模型的神经过渡系统抽象及其应用于计算树逻辑验证的应用
Yejiang Yang1, Tao Wang2, Weiming Xiang3
1School of Computer and Cyber Sciences, Augusta University, Augusta GA 30912, USA; School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China.
概括
本研究介绍了一个可解释的神经网络的基于抽象的验证方法. 它增强了模型的解释性,并使用计算树逻辑 (CTL) 实现了正式验证.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 正式方法 正式方法
背景情况:
- 数据驱动的模型,特别是神经网络,往往缺乏可解释性.
- 正式验证方法对于确保系统可靠性和安全性至关重要.
- 现有的验证技术可能难以应对神经网络动态的复杂性.
研究的目的:
- 为神经网络模型提出一种可解释的基于抽象的验证方法.
- 在验证过程中增强可解释性和用户交互.
- 为了使系统行为与规范的正式验证.
主要方法:
- 状态空间分区使用数据驱动过程来抽象系统动态.
- 使用设定值可达性分析来估计子系统关系.
- 从神经网络模型构建神经过渡系统抽象.
- 使用计算树逻辑 (CTL) 验证抽象模型.
主要成果:
- 提出的方法成功地将复杂的系统动态抽象化为可理解的状态标签.
- 神经网络模型的正式验证是通过构造的抽象实现的.
- 该框架展示了增强的解释性和验证能力.
- 使用Maglev和手写模型的例子说明了框架的有效性.
结论:
- 开发的基于抽象的验证方法显著提高了数据驱动模型的可解释性.
- 该框架提供了一种强大的方法,用于使用CTL进行神经网络行为的正式验证.
- 这种方法有助于对复杂系统与用户指定的属性进行验证.
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