基于样本的连续近似方法来构建间隔神经网络
概括
本研究引入了一种用于训练间隔神经网络 (INN) 的新方法,用于量化安全关键应用中的不确定性. 该方法确保了可靠的预测,保证了信心水平,提高了对错误的稳定性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 量化神经网络的不确定性对于安全关键的应用,如对抗噪声预测至关重要.
- 间隔神经网络 (INN) 为不确定性量化提供预测间隔.
研究的目的:
- 将INN的培训制定为一个机会受限的优化问题.
- 为INN培训中难以解决的机会受限制的优化问题开发近似方法.
- 确保开发的近似方法产生最佳的INN,保证可信度水平.
主要方法:
- 制定INN培训作为一个机会受限的优化问题.
- 采用基于样本的连续近似方法来解决难以解决的优化问题.
- 证明近似方法的统一收.
- 用有限样本调查近似值的可靠性.
主要成果:
- 机会受约束优化的最佳解决方案自然形成一个INN,在所需的信心水平上提供最紧密的预测间隔.
- 基于样本的近似方法实现了统一的趋同,始终产生最佳的INN.
- 该研究提供了有限样本的违规概率,确保近似可靠性.
结论:
- 拟议的方法有效地训练INN在安全关键应用中的不确定性量化.
- 与现有方法相比,INN方法显著提高了回归和无监督异常检测的性能.
- 该研究通过数值示例和风力发电异常检测案例研究来验证有效性.
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