论文标题
使用ISAT验证Sigmoidal人工神经网络
Verification of Sigmoidal Artificial Neural Networks using iSAT
论文作者
论文摘要
本文提出了一种验证网络物理临界系统中非线性人工神经网络（ANN）行为的方法。我们在SMT求解器ISAT中实现了Sigmoid函数的专用间隔约束传播器，并将此方法与组成方法进行比较，该方法通过ISAT中可用的基本算术特征和近似方法来编码Sigmoid函数。我们的实验结果表明，专用和组成方法明显优于近似方法。 在我们所有的基准中，与组成方法相比，专用的方法表现出相等或更好的性能。
This paper presents an approach for verifying the behaviour of nonlinear Artificial Neural Networks (ANNs) found in cyber-physical safety-critical systems. We implement a dedicated interval constraint propagator for the sigmoid function into the SMT solver iSAT and compare this approach with a compositional approach encoding the sigmoid function by basic arithmetic features available in iSAT and an approximating approach. Our experimental results show that the dedicated and the compositional approach clearly outperform the approximating approach. Throughout all our benchmarks, the dedicated approach showed an equal or better performance compared to the compositional approach.
