IEEE Trans Neural Netw Learn Syst. 2015 Aug;26(8):1810-5. doi: 10.1109/TNNLS.2014.2354418. Epub 2014 Sep 10.
This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estimation of the target variance in the bootstrap method. An optimization algorithm is developed for minimization of the cost function and adjustment of NN parameters. The performance of the optimized bootstrap method is examined for seven synthetic and real-world case studies. It is shown that application of the proposed method improves the quality of constructed PIs by more than 28% over the existing technique, leading to narrower PIs with a coverage probability greater than the nominal confidence level.
本简稿提出了一种利用自举技术构建优化预测区间（PI）的有效方法。该方法在使用自举法估计目标方差的神经网络（NN）训练中采用了基于 PI 的创新成本函数。开发了一种优化算法来最小化成本函数和调整 NN 参数。对七种合成和真实案例研究的优化自举方法的性能进行了检验。结果表明，与现有技术相比，该方法的应用将构建的 PI 的质量提高了 28%以上，导致 PI 更窄，覆盖概率大于名义置信水平。
